{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "use this for paper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute the clustering performance for all methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaned and pseudobulked data\n",
    "params = {\n",
    "    'nonzero' : {'yeast_expression' : '../data/processed/pseudobulk_nonzero_logcounts.tsv',\\\n",
    "               'output_folder' : '../results/pseudobulk_nonzero/'},\\\n",
    "    'perc02' : {'yeast_expression': '../data/processed/pseudobulk_perc02_logcounts.tsv',\\\n",
    "              'output_folder' : '../results/pseudobulk_perc02/'}\n",
    "}\n",
    "params\n",
    "\n",
    "choice = 'perc02'\n",
    "\n",
    "yeast_expression = params[choice]['yeast_expression']\n",
    "output_folder = params[choice]['output_folder']+'clustering_performance/'\n",
    "\n",
    "dictionary_comparison = {\n",
    "    'bonobo_top1k': output_folder+'bonobo_top1k/',\n",
    "    'bonobo_sparse' : '../results/new_pseudobulk_%s/clustering_performance/bonobo_sparse/' %choice,\n",
    "    #'bonobo_random': output_folder+'/bonobo_random/',\n",
    "    'sweet_top1k' : output_folder+'sweet_top1k/',\n",
    "    'sweet_sparse' : output_folder+'sweet_sparse/',\n",
    "    #'sweet_random' : output_folder+'sweet_random/', # perc02 this is available\n",
    "    'lioness_top1k' : output_folder+'lioness_top1k/',\n",
    "    'spcc_top1k' : output_folder + 'spcc_top1k/',\n",
    "    #'spcc_random' : output_folder + 'spcc_random/',\n",
    "    #'lioness_random':output_folder+'lioness_random/'\n",
    "}\n",
    "\n",
    "params[choice]['output_folder'] = '../results/new_pseudobulk_%s/' %choice\n",
    "\n",
    "if os.path.exists(params[choice]['output_folder']+'clustering_performance/comparison') == False: \n",
    "    os.mkdir(params[choice]['output_folder']+'clustering_performance/comparison')\n",
    "    os.mkdir(params[choice]['output_folder']+'clustering_performance/comparison/figures')\n",
    "    os.mkdir(params[choice]['output_folder']+'clustering_performance/comparison/tables')\n",
    "    \n",
    "results_figures = params[choice]['output_folder']+'clustering_performance/comparison/figures/'\n",
    "results_tables = params[choice]['output_folder']+'clustering_performance/comparison/tables/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../results/new_pseudobulk_nonzero/clustering_performance/comparison_bonobo/figures/'"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "method_order = ['bonobo_top1k', 'bonobo_sparse', 'sweet_top1k', 'sweet_sparse', 'lioness_top1k', 'spcc_top1k', 'spcc_random']\n",
    "#method_palette = ['#fb9a99', '#e31a1c', '#a6cee3', '#1f78b4', '#b2df8a', '#6a3d9a', '#6a3d1d']\n",
    "#method_order = ['bonobo_random', 'bonobo_sparse', 'sweet_random', 'sweet_sparse', 'lioness_random',  'spcc_random']\n",
    "\n",
    "bonobo_sparse_color = '#e31a1c'\n",
    "bonobo_top1k_color = '#fb9a99'\n",
    "sweet_sparse_color = '#1f78b4'\n",
    "sweet_top1k_color = '#a6cee3'\n",
    "lioness_top1k_color = '#b2df8a'\n",
    "lioness_sparse_color = '#33a02c'\n",
    "spcc_sparse_color = '#6a3d9a'\n",
    "spcc_random_color = '#6a3d1d'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_items([('bonobo_top1k', '../results/pseudobulk_perc02/clustering_performance/bonobo_top1k/'), ('bonobo_sparse', '../results/new_pseudobulk_perc02/clustering_performance/bonobo_sparse/'), ('sweet_top1k', '../results/pseudobulk_perc02/clustering_performance/sweet_top1k/'), ('sweet_sparse', '../results/pseudobulk_perc02/clustering_performance/sweet_sparse/'), ('lioness_top1k', '../results/pseudobulk_perc02/clustering_performance/lioness_top1k/'), ('spcc_top1k', '../results/pseudobulk_perc02/clustering_performance/spcc_top1k/')])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary_comparison.items()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>auc</th>\n",
       "      <th>check</th>\n",
       "      <th>tpr</th>\n",
       "      <th>fpr</th>\n",
       "      <th>th</th>\n",
       "      <th>group</th>\n",
       "      <th>network</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dal82_MinimalGlucose</td>\n",
       "      <td>0.491736</td>\n",
       "      <td>genetics</td>\n",
       "      <td>[0.         0.00826446 0.12396694 0.12396694 0...</td>\n",
       "      <td>[0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.5 0.5 0...</td>\n",
       "      <td>[1.99999762 0.99999762 0.99999159 0.99999146 0...</td>\n",
       "      <td>dal82</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dal82_MinimalGlucose</td>\n",
       "      <td>0.812879</td>\n",
       "      <td>media</td>\n",
       "      <td>[0.         0.         0.         0.10833333 0...</td>\n",
       "      <td>[0.         0.09090909 0.63636364 0.63636364 0...</td>\n",
       "      <td>[1.99999762 0.99999762 0.99999376 0.99998893 0...</td>\n",
       "      <td>MinimalGlucose</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>0.381818</td>\n",
       "      <td>genetics</td>\n",
       "      <td>[0.         0.00826446 0.11570248 0.11570248 0...</td>\n",
       "      <td>[0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0...</td>\n",
       "      <td>[1.99999195 0.99999195 0.99995086 0.99995019 0...</td>\n",
       "      <td>dal81</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>media</td>\n",
       "      <td>[0. 0. 0. 1.]</td>\n",
       "      <td>[0.         0.09090909 1.         1.        ]</td>\n",
       "      <td>[1.99999195 0.99999195 0.99996336 0.99966973]</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gln3_YPD</td>\n",
       "      <td>0.289256</td>\n",
       "      <td>genetics</td>\n",
       "      <td>[0.         0.00826446 0.34710744 0.34710744 0...</td>\n",
       "      <td>[0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0...</td>\n",
       "      <td>[1.9999956  0.9999956  0.99996693 0.99996661 0...</td>\n",
       "      <td>gln3</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>gzf3_YPDDiauxic</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>media</td>\n",
       "      <td>[0. 0. 0. 1.]</td>\n",
       "      <td>[0.         0.09090909 1.         1.        ]</td>\n",
       "      <td>[ 1.99711756  0.99711756  0.97929726 -0.25574764]</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>gzf3_YPDRapa</td>\n",
       "      <td>0.459504</td>\n",
       "      <td>genetics</td>\n",
       "      <td>[0.         0.00826446 0.09917355 0.09917355 0...</td>\n",
       "      <td>[0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0...</td>\n",
       "      <td>[ 1.9725943   0.9725943   0.54390734  0.527467...</td>\n",
       "      <td>gzf3</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>gzf3_YPDRapa</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>media</td>\n",
       "      <td>[0. 0. 0. 1.]</td>\n",
       "      <td>[0.         0.09090909 1.         1.        ]</td>\n",
       "      <td>[ 1.9725943   0.9725943   0.81510105 -0.23617982]</td>\n",
       "      <td>YPDRapa</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>gzf3_YPEtOH</td>\n",
       "      <td>0.490909</td>\n",
       "      <td>genetics</td>\n",
       "      <td>[0.         0.00826446 0.05785124 0.05785124 0...</td>\n",
       "      <td>[0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0...</td>\n",
       "      <td>[ 1.89613148  0.89613148  0.78763624  0.785521...</td>\n",
       "      <td>gzf3</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>gzf3_YPEtOH</td>\n",
       "      <td>0.869697</td>\n",
       "      <td>media</td>\n",
       "      <td>[0.         0.         0.         0.05833333 0...</td>\n",
       "      <td>[0.         0.09090909 0.45454545 0.45454545 0...</td>\n",
       "      <td>[ 1.89613148  0.89613148  0.79622862  0.783231...</td>\n",
       "      <td>YPEtOH</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1584 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sample       auc     check  \\\n",
       "0    dal82_MinimalGlucose  0.491736  genetics   \n",
       "1    dal82_MinimalGlucose  0.812879     media   \n",
       "2        dal81_YPDDiauxic  0.381818  genetics   \n",
       "3        dal81_YPDDiauxic  1.000000     media   \n",
       "4                gln3_YPD  0.289256  genetics   \n",
       "..                    ...       ...       ...   \n",
       "259       gzf3_YPDDiauxic  1.000000     media   \n",
       "260          gzf3_YPDRapa  0.459504  genetics   \n",
       "261          gzf3_YPDRapa  1.000000     media   \n",
       "262           gzf3_YPEtOH  0.490909  genetics   \n",
       "263           gzf3_YPEtOH  0.869697     media   \n",
       "\n",
       "                                                   tpr  \\\n",
       "0    [0.         0.00826446 0.12396694 0.12396694 0...   \n",
       "1    [0.         0.         0.         0.10833333 0...   \n",
       "2    [0.         0.00826446 0.11570248 0.11570248 0...   \n",
       "3                                        [0. 0. 0. 1.]   \n",
       "4    [0.         0.00826446 0.34710744 0.34710744 0...   \n",
       "..                                                 ...   \n",
       "259                                      [0. 0. 0. 1.]   \n",
       "260  [0.         0.00826446 0.09917355 0.09917355 0...   \n",
       "261                                      [0. 0. 0. 1.]   \n",
       "262  [0.         0.00826446 0.05785124 0.05785124 0...   \n",
       "263  [0.         0.         0.         0.05833333 0...   \n",
       "\n",
       "                                                   fpr  \\\n",
       "0    [0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.5 0.5 0...   \n",
       "1    [0.         0.09090909 0.63636364 0.63636364 0...   \n",
       "2    [0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0...   \n",
       "3        [0.         0.09090909 1.         1.        ]   \n",
       "4    [0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0...   \n",
       "..                                                 ...   \n",
       "259      [0.         0.09090909 1.         1.        ]   \n",
       "260  [0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0...   \n",
       "261      [0.         0.09090909 1.         1.        ]   \n",
       "262  [0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0...   \n",
       "263  [0.         0.09090909 0.45454545 0.45454545 0...   \n",
       "\n",
       "                                                    th           group  \\\n",
       "0    [1.99999762 0.99999762 0.99999159 0.99999146 0...           dal82   \n",
       "1    [1.99999762 0.99999762 0.99999376 0.99998893 0...  MinimalGlucose   \n",
       "2    [1.99999195 0.99999195 0.99995086 0.99995019 0...           dal81   \n",
       "3        [1.99999195 0.99999195 0.99996336 0.99966973]      YPDDiauxic   \n",
       "4    [1.9999956  0.9999956  0.99996693 0.99996661 0...            gln3   \n",
       "..                                                 ...             ...   \n",
       "259  [ 1.99711756  0.99711756  0.97929726 -0.25574764]      YPDDiauxic   \n",
       "260  [ 1.9725943   0.9725943   0.54390734  0.527467...            gzf3   \n",
       "261  [ 1.9725943   0.9725943   0.81510105 -0.23617982]         YPDRapa   \n",
       "262  [ 1.89613148  0.89613148  0.78763624  0.785521...            gzf3   \n",
       "263  [ 1.89613148  0.89613148  0.79622862  0.783231...          YPEtOH   \n",
       "\n",
       "          network        method  \n",
       "0    bonobo_top1k  bonobo_top1k  \n",
       "1    bonobo_top1k  bonobo_top1k  \n",
       "2    bonobo_top1k  bonobo_top1k  \n",
       "3    bonobo_top1k  bonobo_top1k  \n",
       "4    bonobo_top1k  bonobo_top1k  \n",
       "..            ...           ...  \n",
       "259    spcc_top1k    spcc_top1k  \n",
       "260    spcc_top1k    spcc_top1k  \n",
       "261    spcc_top1k    spcc_top1k  \n",
       "262    spcc_top1k    spcc_top1k  \n",
       "263    spcc_top1k    spcc_top1k  \n",
       "\n",
       "[1584 rows x 9 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the correlation performance data\n",
    "fn_correlation = 'network_correlation_performance.csv'\n",
    "perf_correlation = pd.DataFrame()\n",
    "for i,val in dictionary_comparison.items():\n",
    "    with open(val+fn_correlation, 'r') as f:\n",
    "        temp = pd.read_csv(f)\n",
    "        temp['method'] = i\n",
    "    perf_correlation = pd.concat([perf_correlation, temp], axis=0)\n",
    "perf_correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>auc</th>\n",
       "      <th>check</th>\n",
       "      <th>tpr</th>\n",
       "      <th>fpr</th>\n",
       "      <th>th</th>\n",
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       "      <th>method</th>\n",
       "      <th>method_choice</th>\n",
       "      <th>gene_choice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dal82_MinimalGlucose</td>\n",
       "      <td>0.491736</td>\n",
       "      <td>genetics</td>\n",
       "      <td>[0.         0.00826446 0.12396694 0.12396694 0...</td>\n",
       "      <td>[0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.5 0.5 0...</td>\n",
       "      <td>[1.99999762 0.99999762 0.99999159 0.99999146 0...</td>\n",
       "      <td>dal82</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dal82_MinimalGlucose</td>\n",
       "      <td>0.812879</td>\n",
       "      <td>media</td>\n",
       "      <td>[0.         0.         0.         0.10833333 0...</td>\n",
       "      <td>[0.         0.09090909 0.63636364 0.63636364 0...</td>\n",
       "      <td>[1.99999762 0.99999762 0.99999376 0.99998893 0...</td>\n",
       "      <td>MinimalGlucose</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>0.381818</td>\n",
       "      <td>genetics</td>\n",
       "      <td>[0.         0.00826446 0.11570248 0.11570248 0...</td>\n",
       "      <td>[0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0...</td>\n",
       "      <td>[1.99999195 0.99999195 0.99995086 0.99995019 0...</td>\n",
       "      <td>dal81</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>media</td>\n",
       "      <td>[0. 0. 0. 1.]</td>\n",
       "      <td>[0.         0.09090909 1.         1.        ]</td>\n",
       "      <td>[1.99999195 0.99999195 0.99996336 0.99966973]</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gln3_YPD</td>\n",
       "      <td>0.289256</td>\n",
       "      <td>genetics</td>\n",
       "      <td>[0.         0.00826446 0.34710744 0.34710744 0...</td>\n",
       "      <td>[0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0...</td>\n",
       "      <td>[1.9999956  0.9999956  0.99996693 0.99996661 0...</td>\n",
       "      <td>gln3</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sample       auc     check  \\\n",
       "0  dal82_MinimalGlucose  0.491736  genetics   \n",
       "1  dal82_MinimalGlucose  0.812879     media   \n",
       "2      dal81_YPDDiauxic  0.381818  genetics   \n",
       "3      dal81_YPDDiauxic  1.000000     media   \n",
       "4              gln3_YPD  0.289256  genetics   \n",
       "\n",
       "                                                 tpr  \\\n",
       "0  [0.         0.00826446 0.12396694 0.12396694 0...   \n",
       "1  [0.         0.         0.         0.10833333 0...   \n",
       "2  [0.         0.00826446 0.11570248 0.11570248 0...   \n",
       "3                                      [0. 0. 0. 1.]   \n",
       "4  [0.         0.00826446 0.34710744 0.34710744 0...   \n",
       "\n",
       "                                                 fpr  \\\n",
       "0  [0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.5 0.5 0...   \n",
       "1  [0.         0.09090909 0.63636364 0.63636364 0...   \n",
       "2  [0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0...   \n",
       "3      [0.         0.09090909 1.         1.        ]   \n",
       "4  [0.  0.  0.  0.1 0.1 0.2 0.2 0.3 0.3 0.4 0.4 0...   \n",
       "\n",
       "                                                  th           group  \\\n",
       "0  [1.99999762 0.99999762 0.99999159 0.99999146 0...           dal82   \n",
       "1  [1.99999762 0.99999762 0.99999376 0.99998893 0...  MinimalGlucose   \n",
       "2  [1.99999195 0.99999195 0.99995086 0.99995019 0...           dal81   \n",
       "3      [1.99999195 0.99999195 0.99996336 0.99966973]      YPDDiauxic   \n",
       "4  [1.9999956  0.9999956  0.99996693 0.99996661 0...            gln3   \n",
       "\n",
       "        network        method method_choice gene_choice  \n",
       "0  bonobo_top1k  bonobo_top1k        bonobo       top1k  \n",
       "1  bonobo_top1k  bonobo_top1k        bonobo       top1k  \n",
       "2  bonobo_top1k  bonobo_top1k        bonobo       top1k  \n",
       "3  bonobo_top1k  bonobo_top1k        bonobo       top1k  \n",
       "4  bonobo_top1k  bonobo_top1k        bonobo       top1k  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split column\n",
    "perf_correlation['method_choice'] = perf_correlation.method.str.split('_').str[0]\n",
    "perf_correlation['gene_choice'] = perf_correlation.method.str.split('_').str[1]\n",
    "perf_correlation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "perf_correlation.to_csv(results_tables+'correlation_performance_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../results/new_pseudobulk_perc02/clustering_performance/comparison/figures/'"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1388.72x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the performance\n",
    "\n",
    "sweet_color = '#377eb8'\n",
    "bonobo_color = \"#e41a1c\"\n",
    "lioness_color = '#4daf4a'\n",
    "\n",
    "\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "sns.set_context(\"notebook\")\n",
    "\n",
    "g1 = sns.catplot(data = perf_correlation.sort_values(by = 'check'), x = 'group', y = 'auc', hue = 'method_choice', row = 'gene_choice',\n",
    "                 kind = 'violin', \n",
    "                 width = .7,\n",
    "                 aspect = 2.5, palette = [sweet_color, bonobo_color, lioness_color, spcc_sparse_color], linewidth = .5, cut = 0, fill = False)#, cut = 0)\n",
    "# rotate xtick labels\n",
    "g1.set_xticklabels(rotation=90)\n",
    "\n",
    "#for ax in g1.axes.flatten():\n",
    "#    for c in ax.collections:\n",
    "        #c.set_edgecolor(c.get_facecolor())\n",
    "\n",
    "xticks = g1.axes.flat[0].get_xticks()\n",
    "# add horizontal grid lines\n",
    "for ax in g1.axes.flat:\n",
    "    ax.yaxis.grid(True)\n",
    "    ax.axhline(0.5, ls = '--',lw= 2, color = 'red', alpha = 0.5)\n",
    "    ax.axvline(11.5,lw= 1, color = 'k', alpha = 0.5)\n",
    "    ax.tick_params(labelbottom=True)\n",
    "    ax.annotate('Genotype', xy=(4, 1), xytext=(4, .9), fontsize=14, color = '#636363')\n",
    "    ax.annotate('Media', xy=(16, .1), xytext=(16, .1), fontsize=14, color = '#636363')\n",
    "    \n",
    "    \n",
    "plt.tight_layout()\n",
    "\n",
    "sns.move_legend(g1, \"upper left\", bbox_to_anchor=(1, 1), title='Network', )\n",
    "\n",
    "#g1.ax.yaxis.grid(True)\n",
    "# Add y axis line for 0.5\n",
    "#g1.ax.axhline(0.5, ls = '--',lw= 2, color = 'red', alpha = 0.5)\n",
    "g1.fig.savefig(results_figures + 'network_correlation_performance.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "perf_correlation[perf_correlation.check == 'media'].group.unique()\n",
    "media_label_dictionary = {\n",
    "    'MinimalGlucose':'Minimal\\nGlucose',\n",
    "    'YPDDiauxic':'YPD\\nDiauxic',\n",
    "    'YPD':'YPD', 'YPDRapa':'YPD\\nRapa', 'MinimalEtOH':'Minimal\\nEtOH',\n",
    "       'Glutamine':'Glutamine', 'AmmoniumSulfate':'Ammonium\\nSulfate', 'YPEtOH':'YPEtOH', 'Proline':'Proline', 'CStarve':'CStarve',\n",
    "       'Urea':'Urea'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis in the paper, no random edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "method_order = ['bonobo_top1k', 'bonobo_sparse', 'sweet_top1k', 'sweet_sparse', 'lioness_top1k', 'spcc_top1k', 'spcc_random']\n",
    "method_palette = ['#fb9a99', '#e31a1c', '#a6cee3', '#1f78b4', '#b2df8a', '#6a3d9a', '#6a3d1d']\n",
    "\n",
    "\n",
    "bonobo_sparse_color = '#e31a1c'\n",
    "bonobo_top1k_color = '#fb9a99'\n",
    "sweet_sparse_color = '#1f78b4'\n",
    "sweet_top1k_color = '#a6cee3'\n",
    "lioness_top1k_color = '#b2df8a'\n",
    "lioness_sparse_color = '#33a02c'\n",
    "spcc_sparse_color = '#6a3d9a'\n",
    "spcc_random_color = '#6a3d1d'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_102232/2687966426.py:28: FutureWarning: \n",
      "\n",
      "The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='width'` for the same effect.\n",
      "  sns.violinplot(data = perf_correlation_original[perf_correlation_original.check == 'genetics'].sort_values(by = 'auc', ascending = False),\n",
      "/tmp/ipykernel_102232/2687966426.py:37: FutureWarning: \n",
      "\n",
      "The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='width'` for the same effect.\n",
      "  sns.violinplot(data = perf_correlation_original[perf_correlation_original.check == 'media'].sort_values(by = 'auc', ascending = False),\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_102232/2687966426.py:69: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax[1].set_xticklabels([media_label_dictionary[i.get_text()] for i in ax[1].get_xticklabels()])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the performance\n",
    "\n",
    "method_order = ['bonobo_top1k', 'bonobo_sparse', 'sweet_top1k', 'sweet_sparse', 'lioness_top1k', 'spcc_top1k']\n",
    "\n",
    "#method_order = ['bonobo_random', 'bonobo_sparse', 'sweet_random', 'sweet_sparse', 'lioness_random', 'spcc_random']\n",
    "perf_correlation_original = perf_correlation[perf_correlation.method.isin(method_order)]\n",
    "\n",
    "sweet_color = '#377eb8'\n",
    "bonobo_color = \"#e41a1c\"\n",
    "lioness_color = '#4daf4a'\n",
    "\n",
    "\n",
    "bonobo_color = '#f8776d'\n",
    "bonobo_sparse_color = '#ffbab5'\n",
    "lioness_color = '#7cae00'\n",
    "spcc_color = '#00bfc4'\n",
    "sweet_color = '#c77cff'\n",
    "sweet_sparse_color = '#d3a9ff'\n",
    "\n",
    "\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "sns.set_context(\"notebook\")\n",
    "sns.set(rc={'axes.facecolor':'#EBEBEB'})\n",
    "\n",
    "\n",
    "f, ax = plt.subplots(2, 1, figsize = (10, 8))\n",
    "\n",
    "sns.violinplot(data = perf_correlation_original[perf_correlation_original.check == 'genetics'].sort_values(by = 'auc', ascending = False), \n",
    "                            y = 'auc', x = 'group', hue = 'method', ax = ax[0],\n",
    "                            #xmin = 0, xmax = 1, \n",
    "                            cut = 0, linewidth = 1, scale = 'width', inner = 'quartile',\n",
    "                            width = .7,\n",
    "                            hue_order = ['bonobo_top1k', 'bonobo_sparse', 'sweet_top1k', 'sweet_sparse','lioness_top1k', 'spcc_top1k'],\n",
    "                            palette = [bonobo_color, bonobo_sparse_color, sweet_color, sweet_sparse_color, lioness_color, spcc_color]\n",
    "                            )\n",
    "\n",
    "sns.violinplot(data = perf_correlation_original[perf_correlation_original.check == 'media'].sort_values(by = 'auc', ascending = False), \n",
    "                            y = 'auc', x = 'group', hue = 'method', ax = ax[1],\n",
    "                            width = .7,\n",
    "                            #xmin = 0, xmax = 1, \n",
    "                            cut = 0, linewidth = 1, scale = 'width', inner = 'quartile',\n",
    "                            hue_order = ['bonobo_top1k', 'bonobo_sparse', 'sweet_top1k', 'sweet_sparse','lioness_top1k', 'spcc_top1k'],\n",
    "                            palette = [bonobo_color, bonobo_sparse_color, sweet_color, sweet_sparse_color, lioness_color, spcc_color]\n",
    "                            )\n",
    "\n",
    "\n",
    "# rotate xtick labels\n",
    "#g1.set_xticklabels(rotation=90)\n",
    "\n",
    "#for ax in g1.axes.flatten():\n",
    "#    for c in ax.collections:\n",
    "        #c.set_edgecolor(c.get_facecolor())\n",
    "\n",
    "xticks = g1.axes.flat[0].get_xticks()\n",
    "# add horizontal grid lines\n",
    "for axis in ax:\n",
    "    axis.set_ylim(0, 1.1)\n",
    "    axis.xaxis.grid(True)\n",
    "    axis.axhline(0.5, ls = '--',lw= 2, color = 'red', alpha = 0.5)\n",
    "    axis.set_xlabel('')\n",
    "    #axis.tick_params(labelbottom=True)\n",
    "    #axis.annotate('Genotype', xy=(4, 1), xytext=(4, .9), fontsize=14, color = '#636363')\n",
    "    #axis.annotate('Media', xy=(16, .1), xytext=(16, .1), fontsize=14, color = '#636363')\n",
    "    #axis.set_ylim(-1, 1)\n",
    "\n",
    "ax[0].set_title('Genetics')\n",
    "ax[1].set_title('Media')\n",
    "\n",
    "ax[1].set_xticklabels([media_label_dictionary[i.get_text()] for i in ax[1].get_xticklabels()])\n",
    "    \n",
    "plt.tight_layout()\n",
    "\n",
    "sns.move_legend(g1, \"upper left\", bbox_to_anchor=(1, 1), title='Network', )\n",
    "\n",
    "#g1.ax.yaxis.grid(True)\n",
    "# Add y axis line for 0.5\n",
    "#g1.ax.axhline(0.5, ls = '--',lw= 2, color = 'red', alpha = 0.5)\n",
    "f.savefig(results_figures + 'network_correlation_performance_ggplotcolors.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now with random edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_102232/1036299503.py:31: FutureWarning: \n",
      "\n",
      "The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='width'` for the same effect.\n",
      "  sns.violinplot(data = perf_correlation_random[perf_correlation_random.check == 'genetics'].sort_values(by = 'auc', ascending = False),\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_102232/1036299503.py:40: FutureWarning: \n",
      "\n",
      "The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='width'` for the same effect.\n",
      "  sns.violinplot(data = perf_correlation_random[perf_correlation_random.check == 'media'].sort_values(by = 'auc', ascending = False),\n",
      "/tmp/ipykernel_102232/1036299503.py:72: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax[1].set_xticklabels([media_label_dictionary[i.get_text()] for i in ax[1].get_xticklabels()])\n"
     ]
    },
    {
     "data": {
      "image/png": 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+++yzA109wiGCJEnYtXs3jhs1Cbde/ghuvVwW0Hbs2JHnmhEIBAKBQCAQCIRChWVZXH311di2bRs+/vjjLp9/8cUXoCgKv/jFLxCLxdQ/h8OBUaNGYePGjT2eY+zYseA4Dt999x0AWSCaMGECTj75ZGzatAmRSAQtLS2oq6tThaNwOIwtW7bgzDPPVEUjAGAYBlOnTkVraytqampSznPGGWdkrMOLL76IBQsW4LrrrsMf//hHIhp1glgc5Zlk4cgXy91VbfPmzdizey8AYMOGDTjttNMOaP0IhwYejwfRKI/Tjv8lxow4AQAwvPpI1NfX57lmBAKBQCAQCAQCoZA555xz8PLLL+OZZ57pIr64XC5IkoSpU6emPbaqqqrH8jmOw9ixY/Hdd9/hqquuwnfffYfLL78cxx9/PERRxA8//IDW1lYAsnUSAPh8PkiShOLi4i7lKdu8Xm/a7el4//33UVJSgp///Oc91vdwhAhHeUYURQAAp9FB5MWcj49GozBYAM4UzRjci9CVziaWhzotLS0AgGJbubrNYa1AY2NTvqo0IPh8PvA8j5KSknxXhUAgEAgEAoFAOCihKArXX389brjhBqxevTrlM6vVCoqi8Oyzz0Kr1XY5lmXZrM5xwgkn4LnnnsOWLVvQ2tqKiRMnwmg04uijj8Y333wDp9OJwYMHo6ysDABgsVhA0zScTmeXspRtNputy3VkYtGiRbj77rtx9dVX44knnkBFRUVW9T5cIPZXeUaxOGIZDmK8d8IRzQAUHT8ohCNBELBx40b4fL681uNwMz1UgtIVWUvVbUWWElW5P1T53e9+h9/97nddVhsIBAKBQCAQCARC9kycOBETJ07E888/j1AopG6fPHkyJElCW1sbjjrqqC5/SiBrQBaRMiUpmjhxIkRRxLPPPovS0lIMHToUgByX6LvvvsP69etT4hvp9XqMGTMG69atSykzHo/j/fffR2lpKQYPHpz19VVUVODZZ59VXfNqa2uzPvZw4PCaPRcgya5qSqDsXBAEARQtgaLjalmFzNdff4277roLL774Yl7rcbhZHLndbjA0A6Peom6zmorgPsTTS/I8DwCHZRY9AoFAIBAIBALhQHL99dfD4/Fg+/bt6rbjjjsOF154IRYsWIAlS5bg888/x/r16/HBBx9g4cKFePPNN9V9R44cCbfbjTfffBNbtmzBtm3b1M+OPPJIWCwWfPPNN6o7GiALSrt27YLL5UrZDgBz586F1+vF3LlzsXbtWnz22We4+eabsWfPHtx44405z/mKi4vxzDPPwGaz4ZprrsGePXtybaJDFiIc9ZF9+/Zh+q9/jalTp+JXF1yQc7BhRezhNDqIYu7CjyiKCeEIEITCtzhSrKLyPZE/3IQjv98PPWdETdMuRPgQmtvrEeZDCIaCqrvkoczhZmFWyHzwwQeYOnVq2hSphA727duHX//6QkybNg3Tpk3DJ598ku8qEQgEAoFAOMwZPXo0zjnnnC7b77zzTtx66634/vvvcffdd+OWW27Bc889h3A4jKOPPlrd7//9v/+HM888E08//TSuvPJKNVsaID+vH3/88QCQIhAde+yx0Ov1oGkaEyZMSDnv8ccfjyeffBJ6vR4LFizA3XffjUAggEceeaTXsYpsNhueeuopVFVV4ZprrkkRtw5nSIyjPlJTU4NwJILLjj4C/9y6E/v378fo0aOzPr5DONL3ymJIFEVQlASKlg4KAUARbPIt3BxuQkI4HEZciuOe567CnbMW49+fvYQte+SsBZFIBDqdLs81JBwuNDQ0pPwnpGfXrl2IRHgMP8mNvd/YsW3bNpx55pn5rhaBQCAQCITDgPPOOw/nnXde2s/uvfde3HvvvV22n3/++Tj//PO7LddiseCBBx7I+PlDDz3UZZtGo8G6desyHjNu3Dg8+eST3Z63u+uZM2cO5syZk7LNZDJh6dKl3ZZ5uHF4zZ77Ab/fDw1N4+LRI2DUauH3+3M6XrHA0Wp0vReOaAkUBcR6YbE00CiCkSRJea3H4SYcxWIxmAxW/PWq5zC4fCQumzYP/++cuQBwUMTG6iv5FioLiXg8jvsWLMDif/wjL+dX7r3D7R7Mlba2NugMFAYd60fRoAhaW1vyXSUCgUAgEAgEwmEKeXLvIz6fD2aOA0VRMHPanIM+C4IADcNCQ2sRE2OIx+M5HS+KInAQWRwxDJPvKgA4/CatkiRBo9FgaOUR0HEGlDuqUVbUc2rMQ4XD7fvuDp7n8cWXX+K999/Py/mV76JQxoJCpbW1FZxJXgzQmQQ0txzaGRAJBAKBQCAQCIULmU31Eb/fD5NWTjFoYtmcLY4EQQDLaMEycurCXK0/VFc1Cr2KkTTQFMpk8XCzQNFoNF36R0wU1M8OdYhw1EG++77S3w6X78TlciEcDud8XEtLM1ijHNydM4lwtnVNNUsgEAgEAoFAIAwEh8eTez8SCARgYuWJkEnD5Cwc8TwPVsOB1XAAcheOYrEYKDoOmpEOiqxqRDjKDzqdDnw0NSB5VEhMSjkuH1UaUA4XkSIb8t33FeGIZdm81mMgaGtrw6WXXoqb5t2U87Etrc3QGRWLoxhCoQiCweABriGBQCAQCAQCgdAzZDbVR/x+P8yJiZBZyyKQo3AUiUTAaXTQMpz6PhcEQQDFxEExEgRByOnYfECEo/xgMBgQigSwv3En9jfuRE3TLoQiAWg0LLRabb6r1+8Q4aiDvt6DsVgM//3vf1FfX9+r4xXB6HCwdAuFQgCA2rranI6TJAnOtnbVVU3539raemArSCAQCAQCgUAgZMGh/+TezwR8PpQnuao19kI40mp04DR6AFBdGhoaGrB27Vqccsop3WZp4/kIaEYCzUiIHARBjgtFODrchASLxYKowOOe564CAGgYFtMmXwKL2Zznmg0Mh9v33R19vQe3bduGhQsX4qijjsZjjz3a6/MfDsJRb/ud1+tFLCZCZ5Lj1nFG+X9bWxuGDRt2wOpHIBAIBAKBQCBkw6H/5N7P+P1+jNQlhCMtC3+7N6fjw+EwOI0BHGtQ3wPAhx9+iNdffx2tra24/fbbMx4fiYTBaCQwGgl8hO/lVQwch8NksRCx2WwAgHkz/w6buRgUReG/6/+NoiJHfis2QBSKYFkI9NXaTgnCHwjkJpIrKGPA4TAW9FY4cjrleEbahGDEGURQVMd2QlcCgQCam5uh1+tRVXX4BP4nEAgEAoFAGAgO/Sf3fiYQ8MNkKQOQCI4dCOR0fDAYgk5jgE5jSLyXY1go8Yp6ypQWDAXBWCUwrIRwjm5u+aBQJvCSJOW7CgNKUVERAMBksGJo5REAAG+gHWVlpfms1oBBLI4OHAeqLQ8Hd9Hejnft7e0AZMEIACga4AyUup3Qlfvvvw8//PAjAGDZsmVEPCIQCAQCgUA4gJDZVB+QJAn+YAiWhKuaWcsiHInkFKQ6EAhApzFCrzUB6IiJEY/HU/5nIhQKQaONQ6ONQ4jGCj5AdqFYGfTUroXA119/jUtnzsRf7r67z2U5HLJlkcffYbHg8TtRXFLc57IPBgpFsDwUOFCCDxGOMuN2uwEAWn3HwoHWIKrbCV1xe1zQaOVx3ePx5LcyBAKBQCAQCIcYRDjqA+FwGLFYDKZEcGFz4n8umdUC/gCMnBk61gAKlHqsYmnUk8VRIBCEhotDw8VzPnc+KJQJ/MEgHO3cuRMujwffbdjQ57LMZjNYloXH32Gx4Am0o6SkpM9lHwwQi6MDx4Fqy8PB6q+3453H44FWT4FKamoNFyWCSDfwPA9bVUR9TSAQCAQCgUA4cBSG+cdBiiLSKBZHFk7+7/P5YLfbsy7DUGEGTdEw6EwIJFzdFMuh7jKlRSIRCNEYxChAM/Gcz50PCmUC35MgVwgkf/fxeLxPbUdRFOw2uyocxeMivH73YSMcEQ4cfRV/FdH2YLgH+0pvLSz9fj80rAS/k4XBGgMfYiBBgsdDLI4ywfM8dFZRfU0gEAgEQj5obW2F15tbzNsDhdVqRWlpbmEo7r33Xmzbtg2vvvpqP9Uqd5YuXYqXX34Z69at6/dz/ec//wHLsjj33HP77Rx1dXV4+eWXsXnzZuzduxdDhgxJ294XXnghTj31VMyfP7/f6tIXCkI42rdvH+677z5s2LABer0ev/zlL3HrrbdCp9P1eKzH48GiRYvw8ccfw+v1orKyErNmzcJvf/vbfq+3MiiYO1kc+Xy+rMvwB/wwaC0AACNnUcuMJjKkRfnMmdKU1ef9G+048gzZBcntdmPIkCE5XMXAUijuKQfDpDVZOBIEARzH9ak8u90OX1CeeAZCPkhSXI19RCBkiyKG9PZeVsa2w2Fy31uRLRAIQBTj2LimAuPOa0HN9xa4G/QwDS5si9J8wvNRmHXxxOtDv28RCAQCofBobW3FH2bNAp+nTNecVosXXnwxZ/HocOadd96BXq/vV+Fo7969+OKLLzBmzBhIknRQeL6kI+/Ckc/nwxVXXIHKykosXrwYLpcLDzzwADweDx555JFujw0Gg7j88svBcRz+9Kc/weFwoKamplsrnQNddwCwcLJgZMlROIpEIohGeZg4KwDAqLWqxyqxjpQsa+lQ4l0cdUYbbBV8yrZCpVAsjgaqj/SFAy0cWaxW+H2yMOkPyf+VbGsEQrb01eJIGdO6G9sKBUmS8N5774HneZx//vk5WxCxLNur80YiEejMAsb83AWDNYaRp7hR+0MMEW/hJ0DIF0JUAMsR4YhAIBAI+cPr9YKPRvHHE45Dtdk0oOeu9wfw6Pof4fV6iXBUYEyZMgWnn346gA4Lr4ORvAtHq1atgs/nw5o1a1TrB4ZhcOutt+Laa6/FiBEjMh777LPPIhKJ4I033lCtk0466aQBqTfQIRA5QxGYtSx8CXU5W/NEZb9wNABeCENDa9HW2gYA8PvklWW/P3OWNiU1s72KB6uLg9VSJF1zlhyMwlFfMZtNqG+V+0coEkhsM/e5XMLhhSIc9TZGkeLiG8gxA2U+8Hg8WLJkCQDg6KOPxujRo3M6vrciG8/zYNg4zMXyfW+wxqAzi/C0Ff64lQ/i8TgEIQaGjYNmiHBEIBAIhPxSbTZhpN2a72rkxJdffoklS5agvr4ew4YNw/z583HssccCkH9nV65ciTVr1sDpdKKsrAwXXXQRZs6cqR6vuJctXboUCxcuxI4dO1BVVYV58+Zh0qRJ6n7ZlKWwdetWPPzww9i9ezcqKytxww03YPLkySn7rF69GqtWrUJDQwOKioowbdo0zJ49O6vFvmuvvRYbN24E0KEhzJ49G3PmzMmq7P/85z9YsGABli1bhqeffhqbN29GUVER/vCHP+CCCy5Qz9Nbwwmfz4ebbroJgiBg8eLFeQ9Hk3fzj88++wwnn3xyisvMueeeC61Wi08//bTbY998801cdNFFWbm09Qc+nw8MReH2z77CXo8PS3/cCi3DZB2gWnE1+9fGJ1Dv2YNWXz1q6+oAAO7EZ93FtHA6nWA0UANj60zxg0Y4SnZzEUUR33//PXbu3Dlg5z8YJhbRJDPX6AEwedXpdOCjspWH8t9gMPS5XMLhRV8zIyqCeb78/3MhWbDtTcbK3gpHsZgAmkkV5mgaEMXCzpqZL5TxkdZIYDTUARkvCQQCgUA4XHA6nVi4cCEuu+wy3H///dBqtZg3bx5cLhcAYMmSJXj22Wfxi1/8Ao888ghOP/10LFq0CM8//3xKObFYDH/7299w3nnnYeHChbDZbLjjjjtSnvlyKeuuu+7CtGnT8OCDD6K6uhq33XYb9uzZo+7z+uuv48EHH8SECRPwyCOPYPr06XjppZfw4IMPZnXd8+fPx+jRozF27FgsW7YMy5YtUwWfXMr+85//jIkTJ2LhwoU4/vjjcf/99+Orr77KrvEz0N7ejmuvvRY0TeOpp57Ku2gEFIDF0Z49ezBjxoyUbVqtFoMHD07pGJ2pq6uD0+mExWLB1VdfjS+++AJGoxHTpk3D7bffPiBiks/ng1Wnw58nHY8qsxHXjBuDv3y5PmtXNeUmuvq0v6PaNgJjKidhu+srSJIEp7MNwyqPxL7G7QgGg2k7S1tbG/QmQNFgWAOPtra2A3Z9/YHi05lsrbBp0yb86U9/AiAru919d/X19fjpp59w6qmnwmrNTclP9ieNRArf5SMajUKvYRCOiQfE4kir1UIQE7GzYrJw1lf3N8LhR19d1dqcrSn/C5lksWgg46LxPI9YlILfKbu6mYsFCFEaonhw+sT3N4pQxGgkMJqDY2GAQCAQCIRCwefz4YEHHsAJJ5wAABg/fjwuuOACrFq1Cpdccglef/11XHLJJbjmmmsAAJMmTUIwGMRLL72EmTNnqgvRgiBg7ty5OPXUUwEAVVVVuOiii/Dll19i6tSp8Hg8OZU1a9YsVciZNGkSZsyYgeXLl2PBggUQRRHPP/88zjrrLNx2223qPgDwzDPPYNasWaiqqur2uocPHw6j0Qi9Xq9aVwHIueypU6fi97//vbpfQ0MDnn/+eZx88sm9+TrQ3NyM66+/HuXl5Xj44Yeh1+t7Vc6BJu/Ckc/ng8Vi6bLdYrF0uyKtWNYsXLgQv/jFL7B06VLs3r0bjz32GARBwH333dfrOmU7MQqFQrBwWtUUUW/SwMZxCAaDWZWhCExHlI2DhmFRZhmE9bUfwuPxgOd5jBlxAvY1bkdzczOqq6u7lNnW1gbW2PGAzBljaGltKpiU991BUZRaz+SH/Fgs1m39X331VXzyySeIRqNdBEeg47tLV0byKjTP8wXfTtFoFCaWRTgm9tgu2aDRaBCPy5NfRURjGKZgApb3J31tu+761cFMb65HERtpmk57fE9t1drSAgBoaWku+PZMFrjj8Xif6ptLW7W3u+Bp0mPjGj0oWsJpf6iDu16HWEws+DbrL7rrV4rARzESGI0EQRAO23YCDt3xqr8g7ZU9pK2yh7RV9pC2yj8mk0kVjQA5lMWECROwefNmbN68GbFYDGeffXbKMeeccw7WrFmDHTt2YPz48QDkZ8OJEyeq+wwaNAgsy6K1VV4szKUsAPjZz36mvmYYBlOmTMHnn38OAKipqYHH40lb1tNPP40ff/yxR+EoE7mWnVxP5f0TTzwBUcz9ua2hoQFXX301Ro0ahb///e/QJmIoFwJ5F44yIUlStxNaZeI7YsQIPPDAAwCAk08+GbFYDAsXLsS8efN6lWqcpumsTcGi0SiMbGoTGjUMotFoVmXwPA8DZ4KGSawq62yIClE0NTUBAMYfORn/+d8/1Zuts8DW5mwFZ+ywROFMMTh3OQvClC0TimKq1WrVeiZbGJlMpm7rr9x8HMd1u186MbKz1U4htxMAiDEBZq0WbeEIWJbtc30zWXKla6tDjQP1XR9qbdWbdlHElJ7Gykz3oNPpgskRRbvTBbPZ3GfXt/4k2fVXp9P1qR/l0laDBw9GSNqLEZM86jZrRQSBekfBj1v9Tbp+pSzCMBoJtEbun4d7OwGH3njV35D2yh7SVtlD2ip7SFvlj3TJcoqKilBXV6eGYHE4HCmfK++TPW04juuSGESj0aiL97mUpdFouvSJoqIitLe3p+ybTVm5kmvZnZ857HY7YrEYPB5PlzJ6YsuWLfD5fLjlllsKSjQCCkA4slgsab9Yv9/fbWBspYMnB9tS3sfjcezZs6dXwlE8Hs+6o7ndbuiZ1DBReg0Dj9udVXazpqYmmHQ29b2Jk1+vX78eWpbD0IpRKLaXY8uWLZg2bRp8Pl+Ku0RLSzOsQzvec0YRPl8ALS0tBdfRFJSbPRqNqm2kxHoCEnGbulFmlZXlSCSSto0ZhlH7VGfXksbGRvV1W1tbwWegC/gDsOk4wCu3S1/rGwwGQdNy2ypB2kRRTNtWhxp9bbvu+tXBTG/aRfnRF0Ux53uwtrYWkiTBMTiMmnYttm3bhurq6t5VfgBQxitAbqu+9KNc2orjONAMrQbHBmRRhOO0BT9u9Rfd9SvFRZvWSKAZ+Tf8cG0n4NAdr/oL0l7ZQ9oqe0hbZU8htpXFYjmsLKCS52IKLpcLDodDFW9cLldKtjblGSkXwS+XsmKxWBfPJKVOnctKpjf16q6e2ZTtdrtTrsftdkOj0fQqe/U555wDhmHw5z//GY8++ihOPPHEnMvoL/IuHI0YMaJLLKNoNIra2tq0rkgKiulbZ5JXw3tLtoNWJByGudNquY5h0B4KZVWGx+OBSdsRp8fEya937tiJQeUjQNMMBpeNwo4dO9R6KeWKogivx48SY0cMDq1B/qytrQ3l5eVZXcNAo6TgliRJvZbOQaC7azvFCo2iqG73S24rBcX1kaKkgvpxykQ4HMZgThYAA4FAn+srCAI0jNxf2YSVWzQa7bEtDwVisdgBcclL168OZnpzLcoYm3wPZyq78+f79u0DABQPDaHmeyv27duHioqKnOswUCTHQotEIn367nNpK47jIMVSH1jjAg2dTndI9b/ekK5fKb8rNCOBYuJ9/q4OFQ618aq/Ie2VPaStsoe0VfaQtsofgUAA69evV93VAoEANmzYgIsuughjxoyBRqPB2rVrceSRR6rHfPzxx9Dr9SnbeiLXstatW6fGOBJFEf/73/9wzDHHAACGDBkCu92Ojz/+GGeccUZKWRRFYdy4cVnVKdkiSiHXstetW5eSeXfdunU48sgjey0+3nLLLYhGo7j11luxaNGiFPe9fJJ34ei0007D008/DbfbrZp5ffTRR4hGozj99NMzHqfVanHqqad2iVj+1VdfQaPRYOTIkf1abwDg+QgcnQQqLcNkndHF6/XCoO1QLI0J4aimpgbHjZgCABhWORrvff1KSmBnQDaRkyQJWn3HdkU4crvdBS8cJZNL2nnlBuyNMKhYSuitYtaZ7/JJOByGw+xQX/eVaDQKDSMLURqNVt12OATI7mt8msOB//3vf9i5cycuv/zybi0Wk8XbXKmpqQGnp2AsEqDVUdi/fz9OOeWUXte5v0mOvzaQAZf1ej3ETsJRTKBg05MsiOlQvhtGI4FmRJJVjUAgEAiEHLBYLLjvvvtw1VVXwWQyYeXKlQCA3/72t7DZbLj44ovx8ssvg2VZjB07FuvXr8fq1asxZ86cnAI351IWy7J48cUXEY1GUVlZiTfffBOtra244oorAMhzwiuvvBKPPPII7HY7Jk+ejB07dmDp0qU477zzUFlZmVWdhg4dinfffRf/+9//UFxcjOLiYpSUlORU9nvvvQeO43DkkUfiww8/xA8//IDHHntM/TwSieCLL74AIHscBYNBrF27FgBw/PHHp3Wvv/322xGNRnHLLbdgyZIlqmCWT/IuHP32t7/FP//5T8ydOxdz585Fe3s7HnzwQZx//vkprmp/+tOfsGbNGmzdulXddt111+GSSy7BbbfdhgsuuAC7d+/GkiVLcOmll6KoqKjf6x4JRxASYtjt9oKigBE2K6KiCD7LDFh+XwA27RD1vZGTRSRnuxNDp8iq5bCqIxEKhVBXV5diFqe407FchzLPcrKIVMiiSCgUApAadLY3wlGyCLBv3z4sX74cp5xyCqZNm5bxWKXN9JYovF5PznUfaELhMCycFhqaRjAY7HN50WgUbEIw0iYJR4cDh3uw3GQ6i9AKixYtQigUwpQpU3DEEUdkPF4RjJLv4WzZu3cvDHYeFAUY7LxqgVSoJAu2vRFve9NGAGA0GhHjU8XxWJSGqYTEf0iHMo7RmjhoJo5IpO9CO4FAIBAIvaXeHziozllcXIzrrrsOS5YsQUNDA4YPH45//OMfqlvYDTfcAIvFgrfffhsrVqxAeXk55s2bh5kzZ+Z8rmzL0mg0WLBgAR5++GHs2bMHlZWVePDBBzFq1Ch1n9/85jdgGAarVq3C6tWrUVRUhMsuuwyzZ8/Ouj6XX3456uvrcc8998Dv92P27NmYM2dOTmUvWLAATz31FF544QXY7XbceeedamY5QHZ5UzKIKyjvn3rqKUyYMKFLmRRF4a677gLP87jpppvw5JNPplg15YO8C0cWiwUrVqzAfffdhxtuuAE6nQ7nnXcebr311pT94vF4F/PFsWPH4tlnn8Wjjz6Ka665BjabDZdddhnmzZs3IHX3eL3Y5/Hgq6YWaGgaqy/8BXa4PPAhu5X4QCCACrNJfc8yWjA0AzEuYmiFPHEbUiHfHDt27EjxcVQEGEbbMTHRaOMpnxUiiqiVLITkIhwplkbJIsCmTZvw7bffgo/w3QpHHo8HWo6C1iDC7XFl3K8QiMViiPA8TKwGJk57QL5TQRDAJiyOFAHpcElbLQhCxuDghxuZ+tJAiIh79u6CoVh2/zLaeezZu6vfz9kXlLYyc1yvhCMlJluumM1mCJ1uzXiUhdlk7lV5hzqqxREjB8cmwhGBQCAQ8oHVagWn1eLR9T/m5fycVgur1drzjkn85S9/UV9Pnjw57T40TWPWrFmYNWtWxnLmzJmDOXPmdNm+bt26PpW1YsWK7qqP6dOnY/r06d3u0x2lpaV4/PHH+1T2oEGD8PTTT2f8vLKyEt98802P5axZsyblPcMwuP/++3s8bqDIu3AEAMOGDcPzzz/f7T4PPvggHnzwwS7bTz311BRFbyCxWsw4yqDDb0aPgOK1cZTDji2R7CyOQsEgdEXGlG2yK1EU5cWDAQAmgxV2iwO7d+9OEY4UgSXi00BKGBCYiuRthWxFosQZ8iUFYUuub7ZCRjpXtZ5cZ7xeL7QG2b3P0+Lpdt98EwjIqwZGLQsjy6rv+0I0KqguakwixlFPQt2hQiQSgdlMJt1AaiaISCTSRVDryQ20t1Y0kUgErS1tGDVK7nPGoih2bWsFz/MF6y4ZDAbBaTSw6rhe3YO9FWbNZjNigoS4CCTi2SMW1RyUffj111/Hiy++iLvuuivjA2lfUWJR0RoJjEZC5DARxAkEAoFQWJSWluKFF19U5zsDjdVqTQnSTCAcSApCODpYYVktzDQw0t6h7Oo1GqSJ2Z2WcCQCnSbVL5SmaNitpahv2YtyRzU8ARespmLU1NSk7Ke4m2z5WM4cR9ESxp3fDKD3E7uBQMl043Z3WPzwPA8tyyIqCFlPtJJFIvV1D8KRy+WCRieA1cvZ5+LxeJ+CqPcnimWWWauFWcv2KaWkAh+JQOAFRPgQ3H45zfjhEoTwcLGsyobkDBEej6dLPLT+Eo7kjGryWCUKFBhtHJIkoa6ubkBi0vUGn88HM6eFmdX06h7sbWwyxS1ZiDDgjGLiNZ3zKmIhoPx21dXV9ds5eJ4HRcsiG62Jgw9Fej6oABFFETU1NRmTfxAIBAKh8CktLSXiTYEgimK3z60aDZFCcoG0Vh/QsBoIQuoDqhCPg8myE0YFHqwmdaVdkiRQEnDPc1fhzlmL8e/PXsL+xh0w2lLLTO7oR0xpR9N2I75/W85OVMixXJyJtMkutweiKIJhGEQiEdhNJrS43VlP8JMHAUU4oumehKN2sLootHoRcVFO2dybNIkDgbJSYdGysGg0B2Tloq2tDU3NTaht3o1/f9q92eehQHIfSc6OdbiTnGLe6XR2EY56Gj8U0TrX4NiKcLDj02LozS1o2mZStxeqcOT1emHltLCybK/iovU23pwiEAkRGpxRhCQB0bB0UApHA7GQwfM8NJrE74BGQvggvd/ff/99PPHEE5g1axYuvvjifFenYPF6vdi8ebP6vrq6GkOGDOnmCAKBQCAcjsyYMQNNTU0ZP8/Gfaw7zjvvPJx33nl9KuNggghHfYDjdBACqavQUVEEl0V0eVEUEY/HoaE7rSpSFKrLRmDuxfeg3FGNy6bNw7/XrUCde3vKbop7yZGnO1E8NAxrOQ8+wOCn98rSRrcXRRG33HormpqbYTaZ8MjChWkjuPc3ba2tqDIZ0RAIwu12o7i4GOFwGHazLBz1tEKvTEKSLWUUC4meLCXa253Q2uIp2ecKXTiyclpYOS1qklz7skEURXz44YeIRCLgOA7nnnsuSstKYdWVYXD5SJw/5TI89vLtGdvsp59+wsMPP4SYGMOQIUPx4AMP9fWSBpxksaiQ434NNK2traApCnFJQmtrq7pdubd6Eo7Sxe0RRREfffQRaJrGL37xi7THNTY2gtMDY85tgsEaw6jJbvz4Hz0aGxv7cDX9i9vlgl3Lwsppsbs997hovRV8OyyO5PtTjFKIx5GSIOFgQelX/SkgRSIRJLxvwWgkRPnCddfuDuX3j4xX3fPKK6/g3//+t/q+uqoKS5cty2ONCAQCgVCIPPLII4dNWI6BgAhHfYDT6RCJpbr68GIcnL7nILyK8OEJtaHOtQsURaHaPhJSPA5Jkj9rcTVgSMUo6HVG8JFUSxxl5Vmji4NhJRisMSDxXJ5ucuHz+bBz+3bEOQ5+jwd1dXUDLhyJoohWpxNnD6pAQyCI5uZmVTgy6XTgWLZH4UiZtCYPAor40ZMFhNvtRlllDFxCOHK5XBg2bFhfLqnf8Hg8oCkKZq0WNo7Djy53Tsfv2rULixcvVt8PHToURqMR8WAMOs4Aq1nOkpAp7frOnTvhdLqgtwj48QcfYrHYQWfOmRyAnUzEOmhqasIQmwXuCJ+yCqPcPz19z8q9lywE1NbW4h//+AcA4Ljjjkub1bK1tRU6SwzmYvl4gzUGvVlMEa8KjXanE0M5Dg69Dt82tuR8vOKaCySC02fpftRhccSk/D8YLY4U+lM44nkejEYun9ZI4As4zl825GrNd7jhcrlw5OBBeGTOlXj1v5/irS+/yneVCAQCgVCAFKpF+8FKYQZ4OUjQ6/UId4oRE47FoNcbsi7jvc0r8ciHc/HYRzcCAGJxAT/s/Ar3PHcV7ls2FwCwfd/3iPCpgorNZgNFAdFgh3UAn3itpE5MRplEu86Vs44diGDLueJ0OhGLxTCutBgA1ElrKBSCUaeDQa/rcYKvWJEkW5Nk85DN8zxCoQi0hg6Lo+RYL4WG2+2GTa8DTVGw6bTweL05TbyU7/eWnz8BQHaZ0el02FW3Gdv3/4Cfdn4NAGmt0wD5O9GbgWEnegD0PlZLPkkWjpJfH+401Neh0qBHhdGA+vr6Lp/3ZHGUzu0vOcB9puD8LS3NoGgBficLv1MWUChNFM3NzblUf0Bpb29HsV4Hh14Hj8+f86pVW8I1F5DHv2zR6/VgGBp+JwtRoNT2OhgtjhTXxp6IRCLYtGkTNm/erLbzf//7X9x7772499578fjjj2fMUheJREAnhCPZ4kgo6Fh/mcjWevZwx+f1ospRBIvRgEGlxQhHIr3OYEggEAgEAiE7Di4TggLDYDAg1MniKCSKsBl6Fo4UsWPqMb/DmMpJHav9NItB5SNxzqQZKLaWo7m9HiWOKhjE1JVqlmVht9sQ8Xe4QkQCGlAUUFxc3OV8ysQ/bpSzuOUj5ktDQwMAYJjVgmKjQX0fCgZRbjXDpNP1OMEPBWVhKVlgUtquOwFJEYm0ehE0A2h1VOELR4lMU3adDlFBQDAYhMlkyup4RThyGMsAyMJJSUkJInwIDy2/CYA8OXE4HGkndsFgEBqtBI02rr4/2DI6JceX6Smwscfjwfr16zFq1KhDPlZGXW0tzq0ohZHlsadT0H0APVrFpBs7kl1HMwVcr6+vh8etx8Y1elC0hNP+UIeIX4N6vv+CJvcFQRDg9npRPHwQivU6SJKE9vb2LjGhuqO5uRk61oCIEEJzczMqKiqyOo6iKLAsi/pNVhQPiaD2J9nS6FC2OHrttdewatUqAMBNN92Ec889F++88y62bNkMm7kYHr8Ts2fPTjsO8TwPWiOPVYxGDrouCEJGi8pChQhH2eHz+TByUBUAwJp4pvH5fGktHQkEAoFAIBwYiHDUB0wmE3x8FLvdXlSZjXBHePiiAkYlHmS6Q3EHicVjKDVXwxtpR51rFxhag3ZvM557637cOWsx/vnuP9DmasToY4Z3KaOquhqbf3KDYuQH8satZjiKi9I+LCtBp+OcLuX9QFJfXw8NTaPMoEeV0aAGyw0FgzCWlcDA9Wxx5PXKAkCyKJBNDA1FJFIyFHGGeEqQ4ELD1d6OIk6ewBfpZAGpvb09J+GIpmgYOSs0DAu/34/LL78c55xzjrqPyWSC1WpNcadRCAaDYFgRDCu36cHo6qX0EVbD9Bik+N///jdeffVVHDf2ODz40IMDUb284PP54Pb6MHj0cBhZFuu271aD1Cv0ZHGk9oWk2y1ZfMxkYaI36KCxBTHouA4Rz1LGg430HBMuHzidTkiShBK9DiUGuY6tra05CUcN9Q04omw8Njd8hcbGRowfPz7rYx2OIvDsbpgcUZQOD2JfuzajePv9999j69atAICSkpKU+zzfKONyT5ZHXq8XZr0d/rAbnkRMN4/bjXNPvhhTxk/D3U/9Hi6XK20bRCIR0Iw8tiuWR5FIhAhHhyh+vx+WxAKd8p8IRwQCgUAg9C9EOOoDRqMRXj6Cm//7BR48bRJe274brnA4q8k9RVGgaRofbX0FR1WciA+3/BPbmzfAyFnAR8P461XPotwxCJdNm4fHXr4NVVVVXco45eRTUVtbA89eeRVaz8UxZfJpac+nWAFIDAPQdF7SsNfX16PCbAJD06g2GfFTwtohEAzCqNPBpNf16EKnTCiSxQ5lQpKNcKTVy9et0UcL2uKo3enEsEQAdEU4crlcWVvD+P1+GHVmUBQFA2dGIBAAwzCorKxU9+lOIAiFQqBZIcXi6GBDCUw8qLS0xyDFipDKH6RBdbNl//79AIAhFjNMLIuoIKC5uRlVVVVZB8dOJ8IliwKZxhaGYUDp42qMIwCyVVusMLNAtrTIMY3KjAY4EnHrlG3ZUltbh0mDzkOLvybndPRFDgeaA7vBsBIoCuB02ozWYE89+yzqk6zHJk+eDEMWlq8DgdI3ehKOeJ5HqbkKGkaj3o9erxdmgxUWow2APO6nGwNlVzXZVUkRuyORyEHn2qfce4WcGTXfSJIEn9+vWhpZjXI/720GQwKBQCAQCNlBhKM+YDKZIIhxPHbGKag2m3DNuDG4Ye3nWVuF6HUGjK88A9W2EZgx4XrwQhhPrbsNUYGHxWiHjjPAwWjQ7mlOG8T5wgsvxIUXXpjVudSHdooCaDrruBMHktqaGgxKPOQNMhvxQc0OxGIxBEMhmPR6mPU6eLsRKERRhNsjiz3Otg5rISW2QUzIHOPA5XKBZgANJ1+3Vh+Ds70t4/75xuVqx4Qy2eWwKDFpzUXoCgQCMGjllXkjZ875oToQ8INh4we1cOTxeGAxGuEwm3sUjpT7obeC6urVq/HNN1/jN7+5GBMmTOhVGQPB/v37oaFpVJqMMCesMfbt24eqqirV1bOnSavi9heNdghAye2WaWzRarUIianupPEYVbBWIYpIVKLXgWUY2PS6nAJ5ezweeH0eVFiHotk7FPv37c/p/BazFQ0u+buI8TRMpsyWrIFAAL5Jp4CvqkbJm6/D7/cXjHCk9I2e7i2e58HSOmgZDjzPQxRFBENBmAxWGPXyWJbpPvb5vRBFCqJAIZbIRJcPd+y+olgiE4ujzPA8D0EQ0locEQgEAoFA6D/I00kfUEzmy40G6DUaFOt1iIpi1rFgzGYzOI0eHKtHqbkag4pGIRaXJ2P1rfsAAE3OWsTjcYwYMeLAVDoxb8tH4ND6ujoMMsuiWrXZhJgoor6+HjzPw6zXw6TTI9iNxVF7eztEUcRQx1EpE7hsrEU8Hg84g6ybAQCrF+F2F6bFkSiKcHt9qqURxzAwarU5udZ5vV4YtbIlmpG15pwWPBD0Q6ONg9EevK5qHo8HdrMJdpMJnjTueMlkO7nNxMdrP8SPP/6E7777rlfHDxQ1NTWotpqhoWnYOC3MOg41CUsVZUzoadKqCJher0fdlo2rmsVsBR/sCPgc8mrAB2lYLLY+XFH/0dLSgiKDHmxCSCs16HOyONq3Tx7DK23DUWEbjr179+U07ppMJohR2cIoFqW7XZAIBgKI63SI62SXunwkPwCAL774Au+++26K0KwI+1kJRwwHNiEchUIhSJIEg84Emmag1xkzCuAtLS3wNOgRaNeifqv8+3swCkfZZgg9nFH6gCIYmfT57fMEAoFAIBwuEIujPqCYwfujAsxaLfyJFfhszeNNJhNC0Y4H4ZgoIBrjoWW1qG/Zi7GjTkJ9y14AwIgRI3LO6JNMvjPMhEIhtLvdqBoxGIAsHAHA3r3y9Rl1Ohj13QfHVrIvHVF2PD7c+jKCwSCMRqM6QQhHMmf+8ng8YHUdExetXoTTW5im7V6vF/F4HLwYRzgWgzvCw8Jpc7I48ni8MGptAAATl7twFAwGwZkk0IwEmj44LY7cbjeKTCY4LGZsbmzqdt++Ckd9PX6gqNm/D4MTVn8URWGI2aQKR8pktSfhSMkOFuEj6j2YTXDs4uISbN2hw8Y1Bow7rwU131sQdHMoPqZrMP9CoK2tDSVJWQdLdBxaE8LRl19+ie3bt+OXv/wlysrK0h6/b98+sBoOJaZKVNmGwx/wyVnaiovh8XggimLaRAYKRqMRopCwOIrSsBnTC0exWAwCzyOu00FKiM35mEQHg0Hcd999AGSrtXPPPRcA1N+tnn6/eD4KljGDZThEo1F1zDHoTIn/xozXpdNxKBkegMkRxdDjPdj0fllG4SgQCKTE+LPb7QVj4aNYHBEyo/QBcyLumIZhYNTpiKsagUAoGFpbW3N+7j5QWK1WlJaW5nTMvffei23btuHVV1/tp1rlztKlS/Hyyy9j3bp1+a4KIQnylNIHFIHIF42iEkb4Emmos7U4stqsCDR3DCzBqPy6tKwM9a2yoFLfug+lJWUwmUxpgxhniyocUTQoihpwV7XGxkYAQFXC3aJIx4HTaNRJq0mvg0mv71agqK+vB03ROKriBHy49WU0NjZi1KhR6oNkMJD5WI/HAw3XMXFh9XFEIjyi0WjBucoocZye37QNo+xWvJYIYKxszwa3y41y3ZEAABNnQ72ra9r17giHwzCwcVAUoOGog9LiyOVyodxiRpHZDFcP1mXZxmHJhCKWFHpK6Pq6ehxX3RHcucpowK7a2pzKaG1tg1VfDG/YidbWVgwbNiyrGEcVFRXgQzSOmOKEKALlowLYtk6fdaaxgaahvh56mlbFW5am0doii9crV6xATW0trFYrZsyYkfb4vXv3otI2FDTNoMo2Qt22ZcsWPPigHID9kksuwc0335z2eIPBgFhUFvNEgYLJkf53RRn/4pxOTX6QD+EoWYxJeZ0QcHpKyBDlozAwHFhai2g0qo45es6o/s90XdFoFMYSOZi/sUge59MJR21tbfjd736Xsm3GjBmYPXt2T5c3IJDYRj2jCETmJFHXZDAQiyMCgVAQtLa24so//AGRPCQhAgAdx+H5F17IWTwiELKBCEd9QLU44uUHVcXiKNuUyXa7DXtqG9X3/ogHAFBVVYmmOllQaWzbjyFDB/e5rsqEVqJpgGEG3DJCsRYqS7J2KDcZ0dQkW4KY9HqYdDoEuxEoamtrUWyuQKVNjvdUV1eHUaNGqbENfP7M6r7T2QZAgihQ4EOMOiHz+XzdrvrnA0Ug/PPJEzDcZsE148bghU3b4M7B4sjtduOIKjnDjEXvgKslN7e8cCiixjditdJBaXHkam/HMaNHodhqQSTCq9Yx6eirxVC27jj5JBQKwev3g0a5KoZoGQaNdY0pFok9WSc2NzbjiLJx+G7/x2hqasKwYcNSBLNMbaC42+78X+r9NnLkyN5eUr9SV1eHQDCIvR4fXtu+G9+3OsFqNJAkCVIWMbH27d2HSstoAECRsQw61oD9+/erAa4piu7W/VQWjuTXoqDJGLNIuTcljkOc41K2DSSZrM4UAacn1zGe56HVaqFJclUDAD0nX7eOM2S8Lj7Cw5IIis1oMrvXKr83njPOQsxeBPt7/0FtjkHL+5OBclETRRHffPMNSkpKMGrUqAE554FC6QOmZOGoB2tlAoFAGCi8Xi8iPI+7L/0thpQNrHhT09KK+15eBa/XS4SjgwRJkiAIQsEZMWSCCEd9QBGOvAlLI28ixk62wlFRURG+j2xS3/vC8uR+yJAh+OnHzQCAVnc9Jo05oc91jSbqqHG5IDGM+n6gaGtrg5ah0RYKQcvQcEd4GBgKbYlYRW0eD7QaBoIgZLQCqq2pRZllCHSsEXZjsWqt5HZ7AMgTj3A4DLvd3uXYlpYW+P1yDIya7y1wN8gPnX6/v+CEI0UIG1vigE6jgd6kQYlej81Zmr2KogiP1wPrSAcAwKIrgj/ggyAIGbMyJRONRhGLiWp8I4aNIxzO7AZYiEiSJLsFWSfAkbhPXS5XRuFIFX5ifROO+uJO2t+0tcnB4P+5bReOKXGoYgiQKjR0Z3UlCAJana342ZBj8FPD56olYbJwlMnq6sQTT8Tzzz+f0kYsy2a0OPrss89Uwbm0tBQ/+9nPsrjKA4cgCLhg5FBVvP2yoRkrtuxAIBBAvAdxTUzEbzvuGNldi6IolFuHoKamFkOGDIbJYEZVybBux2GdToeYIEGSgHiMgT5popyManGk0wEMA0qrzbtwlNwHlLEj3IPVYiQSgVavA8foEA57VeFHp1gcaY0ZrysS4cGwcr9VhKN0QpUiygePORYSp0NkxCi098GS92Bly5YtWLBgAViWxb///e98VycnlD5gTCSNACAvOhHhiEAgFBBDykoxuro639XIiS+//BJLlixBfX09hg0bhvnz5+PYY48FID8brly5EmvWrIHT6URZWRkuuugizJw5Uz1ecS9bunQpFi5ciB07dqCqqgrz5s3DpEmT1P2yKUth69atePjhh7F7925UVlbihhtuwOTJk1P2Wb16NVatWoWGhgYUFRVh2rRpmD17dtbu3z/++COeeuop7Nq1C5IkoaKiApdeeil++ctfAgCuvfZa6PV6nH322Vi2bBmcTifGjBmDO+64IyXT68svv4yPPvoItbW10Gq1OProo3HTTTdh8OAOAxDFLfD666/HU089hf379+Pee+/F6aefjqeeegpr165Fe3s7LBYLjjrqKNxzzz1qjEu/34+nn34a69atg8/nw/DhwzF37tyUtu1viHDUB1iWhUGvhy8hGPl4HhqGyTqbTVFREbwhFyRJAkVR8IadoCgKQ4cORTgSRCjsh9PdkjGGRi4oD+Glr78CmC0D/pDlcrmgZRjc/N8v8eBpk/Da9t3Y1u5BuUZeIb/zhRW4+pdTAcgToaKioi5l1NTUYkLFOQCAMstQ1OyXhaP29nZUlw5HfeteuFyulJTzKpSE8iP9MDmiGHmKG0FXAFvXlhRkXAS/3w8NTYNLclswaVn43dkFx3a73ZCkOKwGWRCzGYohSRLcbndWKxDKRI/RyBMxmhUPOuEoEAiAj0ZRbLWiJCHkOp1ODBo0KO3+qquZ2DtXMzWzXwG7qimT5rsmHa+KIZvbXFjy/aYUN9jurGgaGxshSXGUWqpRaqlGQ0MDgFTBLJN4RlFU+nszDdFoFA888EDKtkmTJkGn02U44sASiUTAR6MYZbNCnxBvj3TIgrTH41GtsjKJbE6nE1EhijJLR38rNVWjvq4WlZUVYGgNGIbttq2Va43HKMRFOuO1K2O5Ym0Ejsu7cJTcLkpdenIlCofD4Er00Gr08ISa1d+sNndjwuqIgtvVVeSJxWKy0J2wOKJogGGptMKRx+MBNBpIWrmtRKMRnpbu458dimQbd6oQCYVCYDUaaJMmBEaOOyjdqQkEAqFQcDqdWLhwIebMmQOz2YyVK1di3rx5+Ne//oWioiIsWbIEq1atwhVXXIFx48bh22+/xaJFixAKhXDllVeq5cRiMfztb3/DxRdfjCuvvBIrVqzAHXfcgbfffls1rMilrLvuuguXXHIJKisr8dZbb+G2227DSy+9pFqxv/7663j00UcxY8YM3Hzzzdi+fTuWLl0Kp9OJu+++u8frDgQCuOWWW3DcccdhwYIF0Gq12LdvX5f54Y4dO9DQ0IDrrrsOAPDMM89g3rx5eP3111Vjh9bWVvzmN79BeXk5gsEg3nrrLcyePRtvvPFGilGJ0+nEY489hj/84Q8oKytDaWkpli9fjtWrV+O6667D8OHD4fF48M0336gLjIIg4IYbboDL5cI111yD0tJSvPfee7jllluwcuXKAbPeJ8JRH7FZLfAk/Fg9fBRWizlrc3OHw4GYKCAY9cHEWeEJt8NqsaGkpAQA0NBWAyEWPSAWMV6vF+A4tFw8E46PPxzw1LU+nw9lRiMWnDoGVWYjrhk3Bq9v341v2z0AgGdvugH+kCxOpBOOAoEAXO52VBw5FABQbhmC7fu/gCRJcDqdmHj0Wahv3ZsxXTYfiaLUFgPDSjBYY9AkVqcL8WEzGAzCqNWm9CMjy3brxpeM4v5i1TsS/+X+43Q6sxKOlAmXMhGjmVhBtlN3KG1QYrWi2CpbHClBndPRV+EnJsjHDbQlXy4oP4JjiotUMUQQ5fsgeVIvCEJG65a6hFtPmWUwSk2DUbO/Vj0m+fi+onxXbb/5LSSaRulrr6CtrS2j8HegUYJaWrgOy0eLVrbW8/l8qnCUya1PsZSKxnjwQhjeSDs0jBZNTU0QRREaDQuG1nTb37iEECTGKMQFSn3fmQ7hSBaWJE6Xl3gvyW0RT3LlCwSDMGURvDgUCkKvNUEvBNDgD6nX9cCLN+LOWYtR37IXtLarUKcK3WzHZxoWacVur9cLytCRXjOu18NP0rgfVITDYeg73QsGjkPrQfYbRSAQCIWEz+fDAw88gBNOkL1cxo8fjwsuuACrVq3CJZdcgtdffx2XXHIJrrnmGgDyYl4wGMRLL72EmTNnqkYTgiBg7ty5OPXUUwEAVVVVuOiii/Dll19i6tSp8Hg8OZU1a9YsXHDBBep+M2bMwPLly7FgwQKIoojnn38eZ511Fm677TZ1H0AWdmbNmoWqqqpur7uurg6BQABz585VxZcTTzyxy34ulwtPP/20aj00atQo/L//9//wzjvv4Ne//jUApMSsFEUREydOxNSpU/HJJ5+o+yhtvWjRIowZM0bdtnXrVkycOBEXXXSRuu3MM89UX7///vvYuXMn/vnPf2L48OHqtdbV1eGFF17A3//+926v80BRGKlEDmJsdjs8CYsjD8/DZuvqJpUJRRDyhGQXEm+oDcXFxbDZbACAJqdsUZNtlrbuaG9vh2SxQCgrR9RkRls3k+j+IBwOQ5fIXNPgDyIci6HSZAQvRGHU63HU4EEoscnXmW7SkzxhBWThqKWtBe3t7QiHQzhi8LGgKCptumxRFBGNCikTi0JOMx8Oh6FnUzVdvYZBhOezCt6sTLpt+g6LIwDdxlPpfH6gYyLGsHGEwoXXTt2RLBxxLAuL0dBtVjpB6JtwFE3EN+Oj+QmGmA2KqNXgD2K324s9Hi84jWzVFolE1Il/d0GMa2trYdRZYOJsKLcMRl1dreqfzWrYlPP0BcWtTjRbIJrlcSGTKNwfKOOCMcm1U3kdDAZ7DKau9L8XvrgH9Z49eHPDE/hyzzvwB3yIRqOgaQYM3X2sOUUoiscoiGJm4Uipq5RY8RK12ryMa+lENGWBYkh5GbzdCDQ8z0OICdBrTTBozfAH/AgGg9DrjPjrVc9hcPlIHDtyIrRsVxfmzkI3AGhYKW0beL1eiPoOi+C4wQCB53uMv0QoHNIJR3pOe9BZxRIIBEIhYTKZVNEIkBM9TZgwAZs3b8bmzZsRi8Vw9tlnpxxzzjnnIBwOY8eOHeo2mqYxceJE9f2gQYPAsqz6DJdLWQBSwhQwDIMpU6Zg82Y5nEtNTQ08Hk/asiRJwo8//tjjdVdVVcFoNOKhhx7Cxx9/nDER1fDhw1NczgYPHozhw4erdQGATZs24YYbbsDPf/5znHLKKTj99NMRCoVQ2ykJjc1mSxGNAGD06NH46quvsHTpUmzdurXL8+U333yDkSNHYvDgwQlLa/nvhBNOwNatW3u8zgMFsTjqI0WOYvx323Z82dgCPhZLq1JmQrEs8oTaUG0fCU+4DcVDitU4LO1e+SZTfBv7QnNzC/jEBEy0WtFYn1smpb4SjUbhjkRw83+/AABoaBq/G3MExJgIR6Je5oTCnG5lWhGOSs2yv3CZZRAkScKWLVsAAOXFg2GzONSV/mTUiYWmY2JBM5njYOSbaDQKhqKw2y1bPVAUoE24rQmCkHECqdDe3g4Nw8LIyWaRetYEVsPlLhxplBhHEiIZhKOffvoJf/7LXxCXJMRFEcOGD8cTixdndZ7+RBHPHBY5E1Wx1dqtxZHquhHL3VpGEU4AgOcLrz8pKD9C8z/9CoB8Dz57zukpnwHpLTUUampqUG4ZnIjZMxSBYAButxs8z4PT6gGkdxHKFeUBI2Y2AzQNUJQqJg0EShu0hcJgKAoUBVQkAvuHw+EehSOfzweGZnHJxD8CkoTJIy/AURUnYvX3zyASiYCmaNA0g3g8c39ThSORgiigW+GI0mrldgIQy1OMo3QB1pVMkCMrK7Blf03G+HWKhZdRawEvhOEP+OD3+2EyWDC08ggAQIm9Aj/u6ToOKQKR4loLZI7L5vP5EEty+YvrOmLdDZQbJKFv8DwPDUNjR309BpWUoN3nQ5iPIkKEIwKBQOg1itFCMkVFRairq1PnZQ6HI+Vz5X2yFwvHcV3iqWo0GnVRMZeyNBpNF+OJoqIidT6j7JtNWZmwWCxYsmQJli5dir/97W8QRRHjxo3DH//4xxT3r3QhVJLr0tzcjBtvvBFHHXUU7rjjDpSUlIBlWdx8881dFlTTxeKdNWsWaJrGO++8g2XLlsFut+Oiiy7ClVdeKYez8XqxY8cO1ZIrmYHMyEqEoz7yu9/9Dsccc4z6/rjjjsv6WJvNBoZh4AnJE1pPuA0jS8aqD7C+gGwhkcltJBdqG+ohlMlpuGM2G9q/34BYLJZ14LADQYlBj/kTxwOQxZBNbe2QJAmWhGCk/E93ozc2NsKqL0KLrw6l5mpQlDxJ2rlzJwCg1F6JElslGhu7xqtQblg6STiiKIDRUAXpWhSLxRCMRlNEtltOGAsAeOedd7B7924AwDHHHINp06Z1Od7lcsHEWVDv3o1SczW8kXaYOEvWwpFicaK6qmkkhIPpH8q3b9+OKM/Dc+bZ0O/cgT27dnWbvWygcLlcsJpMYBP9u9hs7vb6o3yHD7EScyxbeJ6HJAGMNo5wAVtmKff6jeOPRaXZCH80ir0eecKe/CPfnehQs78WVWZ5laTcIgcErKmpkePTaHWgqAMjHDmdTtmlSMlAZjINqHCkCIEPffs9APkefP0COb5aNBpFPGEplEk4CofDYGgaL339IACAoVlce/rf1eOzsThSvpO4SEGMSRkD24dCISBJVBK1XNZurQeSdMKRcs8pAULdbnfamH2KwGTW2SGIsmWl2+2GUWdW9zHozfAHAl0smzosJJMWBjLEZfP5/anCkb5DOFIWcg4HBip7W3/A8zzCPI85jy3GE9dfixUfrcV3O3ai9DD6/ggEAuFAo/wOJ+NyueBwOFTxxuVypYS8UH7jc/GMyaWsWCwGn8+Xsk2pU+eyksm1XmPGjMGiRYsQiUSwYcMGLF68GLfddhveeuutlPN2xuVy4cgjjwQAfPXVVwiHw3jooYdgNptT6t+ZdL/BWq0Wc+bMwZw5c1BXV4f/+7//w9KlS1FZWYlp06bBYrFg5MiRWcVt6k+IcNRHBg0a1Ou4GwzDoMjmgCcsT4g8ISeKi4vVFdlgRHbZyiYTVnfEYjG0NjcjdqQ84YsVORAXRTQ1NQ1YzBCNRgMx8cBfZTbCHeHRHAyDoihYEyv5Wo0GBp1OXX1OprW1FRTF4JEP5+LGsx7HB1v+CZpiUFdXB4PeBJPBglJ7FZoa93Y5VpkEKlZGCoymMIODSpIEC8fhryfLJqMUBTQG5Ingv954A+7E4L5506a0wpHX64UkQW2rD7f8E3FJStuu6VCFNkZS/2cS2FpaWhAvLUPg+BMQLa9A6SsvobW1FcOGDcvpmg807e3tKE76wXBYLNjbjataNBoFQ7MQ40IiBk32Q6MyQeUMsYJ2l1AE6MXfb8KDp03Cu3tr1axqydYWmWLRiKKIxsYGTBgr9zmHqQIahkVdXZ3siqo1gKaZA+Im5XQ6VRc1AIiZzAMqHCmC0J9PnoAinQ4UBdCJH3pJklJi+GQ6nmP1uPHMxwHIDwm8EFY/Y2gGNE0jmoWrmijQkKTuLY6UYM8AIHFa+Askw1RbWxtoisJRgwep79MJR8oDmVXvQCxhheV2uWFIEo6MOhPicRGhUCilv6rCUVL8I1qT/l70BQKIGzvKVOJC5SMmFKF3CIKAUqsVD82ehUElJbhp+q+wat1n+Hzr9nxXjUAgEA5aAoEA1q9fr7qrBQIBbNiwARdddBHGjBkDjUaDtWvXqkIJAHz88cfQ6/Up23oi17LWrVunxjgSRRH/+9//VIONIUOGwG634+OPP8YZZ5yRUhZFURg3blxObaDT6XDqqaeioaEBjz32mGxNn3j22rt3L2pra1V3tdraWuzduxczZswAIC9qUBSVMn9Yu3ZttwuEmRg0aBDmzp2L1atXY//+/QDkuEtffvkliouL87rQRYSjPFNcUgxPyAleCCMcDaK4uBgMI08qIrz88N9X4aipqQlxUUQsodAKif+1tbUDJhzp9XpsD4Zw83+/ULOqfd/qhJZlYU9yxbOZjGkFjnanC9X2kZg9+R6Umqtx0YTr8fSnd8gTkSI58FlpUSV+3P15l2OVuDVhPwO/U25Lc7EAUFJBZsGiaRo0TWGkvSMCf4Nf7gvBYBA3XngBxHgcL3z4cdrjvV4vSsxVmDNlAUrN1Zgx4Xq8sX5x7sKRklVNI4Hn0wtHzS0t4BVlPSHUtLS0DKhwtH//ftzzt79BiEYhSRKqqqpgtljgMHf0K4fFjA2JLHzpiEajMGrN8EVcsitEDsKRYqHDmUQEPYUxYU+HsgJy+8Txala171va8MyPW2GxWFRLjkz9xOl0QogJqrsoQzMoNlegoaFBjUfDCJoD4ibV2tYGPmlc4E0mtDmzs5g7ECirQYMtZpQnhO1YQiyiKApCD8HUGYaBGJcfFhSrv5r2bQBk4YimGdA0AzGW+YFCWUAQeNm6sjuLIzHJ/Suu5RByDVxbqedNsr5SXre1taHIakGFQzbxzhSnyul0gqEZmHQ2xOJym3o8HjgMHWlujXp5fOnsVqYIlZokiyNGE0co1LUf+nw+UJweVDQKJhgA4/OmlHG4cDBbHMViMeg5TrViM5SUoKKoCLFePJwTCAQCQcZiseC+++7DVVddBZPJhJUrVwIAfvvb38Jms+Hiiy/Gyy+/DJZlMXbsWKxfvx6rV6/GnDlzcvKMyaUslmXx4osvIhqNorKyEm+++SZaW1txxRVXAJCfta688ko88sgjsNvtmDx5Mnbs2IGlS5fivPPOyyqT7+eff47/+7//w+mnn47y8nK0t7fj9ddfx9ixY1MW7IqKijB//nxcddVVAIBnn30WJSUl+OUvfwkAquC2YMEC/PrXv8a+ffvw8ssvq8/ePTF//nwceeSRGD16NHQ6HT7//HP4fD613GnTpmH16tW49tprcemll2Lw4MEIBALYsWMHBEFQs731N0Q4yjOOYgd2/bQDe51ycC0lYLZGwyLCyw+z6WJC5IISH0gRjOIGIyidDvX19X0qNxcsFgtYhsHjZ5yqZlVbvnk7NjrdsJs63JpsJlN64ai9HaWM7BPaFmhAtX0kzDo7vN5WjKyU3QNL7ZXwB/xdLCaUSczuL+Trp2gJp/2hLuWzQoJlWYjxVOsoIVHPqCDAajQiFhcRjkTSxgwJBIKw6IowqGgUAKCUrYZFV4RgoGvg8HR0WBzJ72laQixD7J+m1lbEimVT07jRBDDMgAYxBoBdu3ahuaUFM44Yjg0tbdi0ZQtGH3EEhlo6Busis1lNo55u0sTzPAycRRWOcnG1U6wV9OYYPA2RnF3dBgrFtNfAatSsalvb5SCAHMep90KmIOJNTbIbaFTsyBRm0FjQ2NgEhqFh1FnAMpEDYr3R6nRCtNjU93GTCa3OgbM4UkQaJesc0HEPsiyrxsLKZLGo1+sRjgZTrP62N28AIE/aGZoBQ2sgRjOPP8p9HUsIR91ZHCULR5I2P4GC0wlHLS0tKLfbYeA4WIyGjGNDW1sbrIZi0BQNq94BiqIRCAYxuKhDPDTo5fs5EAikrLZ1xLBLjnEkIRjq2g/9Ph+MbW0Ijj0Olq+/hG7/PgCFIxxlytJH6CAWi0HTKZ4DQ9OqUEsgEAiFQE3LwD4L9/WcxcXFuO6667BkyRI0NDRg+PDh+Mc//qE+O95www2wWCx4++23sWLFCpSXl2PevHmYOXNmzufKtiyNRoMFCxbg4Ycfxp49e1BZWYkHH3wQo0aNUvf5zW9+A4ZhsGrVKqxevRpFRUW47LLLMHv27KzqMmjQIFAUhWeeeQYulws2mw0TJ07sIsSMHj0aZ5xxBpYsWYL29naMGTMGt99+u/psNnLkSPz5z3/GsmXL8Mc//hGjRo3CAw88gD/96U9Z1WPs2LFYu3YtXnnlFYiiiMGDB+Pee+9VA41rtVo8+eSTWLZsGZYvXw6n0wmbzYYjjjhCtXoaCIhwlGcqKsrx+ef/wzOfyh1LMeOnKQq76mQxqa8WR42NjaC0WsQNickwRSFmL0JDQ0Ofys0Fu90OLx9VrWj0Jo0cUDkehz1JjS0ymdJGtHe5XWgIr8P3devA0Cweu/hdaCgNQqEQOFaHCB8CTcvduaGhIa07xKgp7TA7OixnCnBuD0A2lQx3smTgRREURUGSJFiNBoiJiZnP51PFRoWAPwCTaEKdaxcoikK1fSTiUjzrCb1iRUHTHa5qsTSWEZIkob2tDeLwRPA4igIslgF1KQJkCxmDlsXvjzkS40uLcffn36K9vR1DzEaEeB7tPh8i0SiigoBwOKym+UyG53kUc2Xq61xQ/Jf11hgkSUIgEMh6hWEgKS4uBk1RaA11iAqtwRDsVkuKWJvp+1O2v/D5PaoYste5GaJhKAwGA4r0g8BrtFlbtnWHy+WCWNGRQlU0meHetbPP5WaLYtGSfB9GEvcAx3FqFr1MLpyyBVccN575OKptIzBjwvX4bt/H+HDrywnhSAOG0SAmZrZ4VB5GFOEo0wKCx+NBXBRVKxqK5xHOgxCSbI6tvG5pbkZlIghkeVFR2qyXgGyJZDfIYhBDM7AZHQiHwjDoOoQjY+J153EsHA6DogAqSUtg2Dgivq6xtiRJgv+EEyGUlsFz1s9B8TzK/rmiYFxMC1FwLjTi8bjqNqrA0HRBLgIRCITDD6vVCh3H4b6XV+Xl/DqOg9Vq7XnHJP7yl7+orydPnpx2H5qmMWvWLMyaNStjOUqMns6sW7euT2WtWLGiu+pj+vTpmD59erf7ZGLIkCF48MEHs9r3/PPPx/nnn5/x82nTpnUJIbJmzZqU98ltnczll1+Oyy+/vNvzm0wm3HTTTbjpppuyqm9/QISjPDNz5kwcf/zxkCQJJpNJFTymz5iB3bt3oby8vM/CUWtrK+ImM9jWjod23mxBY1PXQNL9RXFxMcKCgKAgqGmtW8I8hFgsxVXNbjJhW1vX7FcMTWPyyPMxafhU9eFaxxrB8xF8/sN7mDJ+Kj75bjUAWShLJxwZLDHZRS1BoT6kGwwGBKMCdru9ajyoWl8AOq0WYZ6HzWRSzfK9Xm8X4cjtdmGfdy82NXypimz17l2IUOlj13RGEY4CLhYGawwCTyMmdJ3ghkIhRCORlFg0gsncbfay/sDn88GSmGRbEpNrj9eLDzd8jwtOnqQGTwUSIlMG4chkkX9ocw3urAQUNNgE9RyFKBxpNBqUFhejKdAhKjQFQ6iorFQn9KOrqzJO7t1uN/RaI6772cOqC+S6HW/hp+ZPERWiGDLsWGijEdS01/WpnvF4HEGfD/Ekqy/RYEA4EBiwgP5KJsudbg8GWUxwR3js8yYEQr1enaRmEhmV7BsmnRUcq0cpWw2GZmC12CCKIhhGAw2jgRDtOaua0IPFUUNjI3ROJ9jWFtWKJgoMuOVbsvWV8rq5uRkTJsgJESqL7KrVWmca6hugpW1JlmxmtPg94KMR7G/cCYqiYLfIwlJnV8hIJAINS6UsBDAaCaFw6n0sSRJigoCY3QFJq0VMK39HlFZbkEkS+pOD2bJJkiTQdGq/pigKUvzgvSYCgXDoUFpaiudfeOGALKL1BqvVmhJ0mkA4kBDhKM/o9fq0wbsuv/wy9XVfH/7dbjdEMYayl5YDACSGQWDsOLQ1DZzFUXm5nNFtr8eHETYLJAD1PlnISBGOzCZ4du3ucjxF0bAZSlX3KwCIS/Lk7dKpN2Jw+Uhccd4f8ZdnrkRzc3PKsXQiTXXnZ2VJ6viskDCZTAgLQpd4UDarFWGeh9VoVC2O0v0wmUxmDLOMxVlH/VbtO0OLj0Z9ZEtW51cmxRvXVGDceS1w1hjSWhwpApGY9P0JRhNaBtjiyOfzwZwQIy1cwr0nFsPvfn4mRlZV4qbpv8K22jrc9/Iq+P1+VFRUpBwvSRIifBhmnWwZ0RvhSKujoDPJ4prb7UZ1Iv5GoVE1eDB21+5HKDGx3+Pz46ijjkFTUxO0LItjhg3F13v2pT3W5/PBpLOmuEBWWofhq73vICbGYDHawWsjcO/29KmOfr8fkiRBTBL44kkZF9OlRD3QKKt1z/64FcOsFvUeBFItfzJZ9ijCtSvYjDLLoMTrFpSWlkIQBLAaLViNttvg/CzLgqIAIdK9cMTpdAgdcaRqRaPbsR22zz9DJBI5IBk5s0UR0cycLMREo1G0u1yoTMQ3qihyYPvWbWmPbWhoQCi8C/WePfhwyz/R4JGTHKz9bjXWfrcaGobF03e+C6CrcMTzPJhOayu0RgIfTRX1BEEAJAlSZ+GRZXO2MuwvFEut/hZ2iHUOgUAg9B+lpaVEvCkQRFHs9jd1ILOLHwqQ1joM8AeDiDqK0X7Br9Vt+l07wQ/gKmtVlex28qf/fdPlM5s5KcaR0Qivz9dltZxhGMTjndy3ElmKjhp2PHScAeWcAUW20i4WE8qgIMVTBTgpLpdbaCiT1ntPPVENYvz8T9tQK1GA1wubyZjiqtYZHaeDnjKniGxahgOnTT/x7IwoiqBoYPwFTTBYYygdHkTt912t3hSXQtHYIRyJRiNcAyhIArJ4ZmHl79ii7ajnyMpKGDgOhpISMAmBMF3gZp7n5Ux2OnmCm6vbisvlAmeIQ2uQJ31KGtBCZNiwYfjXhg34f//3kbpt6tChaGhoQFVxMYaUlmLNl1+nteyJRCLgNKlCBKfRIR6PIxwOwWZ2gI9G4PV6+hR0XolRRofCqvsVnRBo/H7/gAhHOp0OnFaLcwdXqvfgFw3NWLllhyrgFJnNGWPjlJSUgGE0cAY6LGycwQZUH1WBUCgErYYDq9EiGs0sWFAUBYZh4G6U3eYyiUBRIQbRalWtaGKJ+D/hcHhAhSPlvinScQiFQuo4XJH4vioddrS2tSUsrjrGXVEUEeEjOPuo36pufR9segnraz/BhT+bheOOODmRqYQFTTNdhF2e58FoOmfMlFR3QgVFpJM6j/kMUzDZNXMVrXvLwS4cRaICdiRiNFKQXbiJvRGBQCAQOjNjxoyM1s4A8M03XeelnXn66acPZJUOaohwdBggiiKg5SCUlavbdHv3DOjDo9lsxsMPP5wSv6i2thb//Oc/YTV0CEdWoxGCIIDn+ZTMORzHgRdTH6rDghzrwm52qNtsJkeXibsyAY6LqcKRKPY9flR/YLPZ5P86Tg1iLEoStJwOeo4Dx7KQJAlalk1rcaTltBDCnVbbxSi0XHZB1uPxOGiGUt36tAaxi7UW0GHtFDd0TE7jBkNaMas/8Xu9KEkIRizDgGMY8KIIQ5KFhiGRdjvdRF+Z8Fr0vROO2traoNFHwbASNCxV0MLRxRdfjCOOOEJ9T9M0JkyYgL/99a8YXFKMQaUlEEURzc3NXaymlFWbOtcuALKwQdMdk3CbuRhRIYK4FIfL5er1vaWIe0UfvY9WhyMliPGByNiWLcUOByhAvQdZmgKn1arjZoWjKGPcMIZhUFFWgVZfh9teq78ek6qPw08/bYKRtULL6hDpwdKlqMiO1oSlkynJsi+ZSCSMeHJwbFZ+PdBxe5TvpkSvQyAQ6CIcVRQVIR6Pw+l0prgS+3w+xONxDHUcpbr1lVnlbGpVpUMxtLKjv+o4fRdxJRqNgmZSByiKkSB0cq9VxEyJSbUylWimV+ly+wPlO+tvAakQs4lmC03TaHG7MeexxQDkMX/OL3/RxX2NQCAQCIRHHnmkYBaHDgWIcHQYoGVZwB8A29KMmL1ITUPMDLB53jHHHJPy/tNPPwUAmJOEB5NenuB3TrlsMpkQjnYKiioEwTAa6JMCqFqMRWjvlLZbcS2RkuYGkgTEY1KfM9b1B0oGA3eEx7BEfDtXNAqN0QxrIgMdRVGwGo1phSOjyQif24061y41Fbgv4oLJnn2msGTvyEyP44FAAKBpSEmWTHGdHuFgMJFyfGDcAH0+H0boOr5HA6cFHwpDl/Td6hOv07mkKGKSLRGcN9cMS61tLeCMAigK0JniAx7jKRfMZjOmTJnSZXttbS3GTzwBQxMT+pqami7CEU3T8EfceOTDuQAAhmYxc+It6ud2SwmigjzhbWlp6bW7njJpbrtwuup+xbhdKHnrXwMqhpSUlaGttcPttS0cQUlxsSoWVRc7sH7v/ozHDxpSjZZ9NQCAIO+DP+zBoEGD8O0336LIooNOqwffg0Dw2GOL4HQ6YTKZVEG5M3wkAilJpIsnhKOBsl5R8Pl8MGq1sHMcajwetLa2gqZpFFvlGGhliSDZLS0tKcKREiPMrLer2wxaeUxnNanjs1bTNR5RLBaDBAl+Z0cb0IyEuBhPsVxVF0o6j0s0VTDCkdK3epOZ8JNPPsFLK/8JSZJgNBnx3HPPZtz3YBaOGIZBscWCe66Q3fkpUFi/cxcYhjzOEggEAiGVkSNH5rsKhxTkl/YwwGI2g9u1C4aXlqP1t5eqK/jmoUPzWi9lEljX2gaKpkCBgkHJZtRpgmiz2+BqaE0RQ8JRP0x6c4pLm9lgRa27PuVYRYASYx0TBikui0fJ4pTCf/7znxSzRo1GgwsvvBB2u73Lvv2B3W4HRVFwJgV3bQ9HUOqgU+JB2UzphSOLxYxdvh9TUoG3+Opw3OijD2g9Q6EQoNWCbW1RBUkqEgIkCeFwOKeU9n0hEPDDZOmYiBoYBm4AXNJkWptwZUsXBFcRioxaCzQMm7Nw1NbWCtsweeLJGqIZU44D8uTV5XKBYZgB60894fP54PZ4MLS8DEVmEyxGA2pqanDqqaem7KfT6aChWVw+6Q7YDWUIRr2obe/IdFZkKUFUkIW5vghHirgnlFeo7lfxhMXYQAYxLi8vx67a/er71mAYZRVV6j03pLQUn/zwU8Yg1IMHD8a2n9YCAJp9teq2UDgMXYkBOq0eUSHa7STe4XCoQnI64vE4BJ6HlGJxJPf7gbY4crvdsOk42HQcfnC64XQ64bBY1NTpJTZZBe8srCrtadR2BNnXsfLYoekUvIhhNF3aSxRFCBE5JhsAULSEIya7AMgCiWL51hHjoNN3RRVORi7FTbM3wtG3336L5pYmnHD06Vi/9VPs3Lkz4wNz8urrQAdR7ysajQagKIxOGl++2rYdLIlTQSAQCARCv0J+aQ8DiouLwYBCy+W/R8xeBM9ZP4f9/XdRloiFkS+i0ShoisJVi5YAkE3On7rxOvWzZIqLHdi9db0qhnyw+SXwsQhsWgf2N8qTV4qiYNCZ4POmukppNBowDA0x1vFwLAqyiNRZOIpEInjyySflN45iWV1ytaOkpATnnXfegbv4bmBZFjaLBe0J4YgXRfgiPEokCdakgMG2DBZHdrsdNM3g1nOeUrNfPfPZnw64UBGJRIB4HGWdBElA/v4GQjiSJAn+QBDmpNhGeo08Ua13OgFKXpE+oroKNEWlnaQrLjZ6rQkGrSknd6hoNAqvx4+yRGBsziigpTWzL/Xy5cvxxhtvAADuvPNOnHbaaVmfq7+orZVFjWHlZaAoCkPLylBTU9NlP5PJhADvxUtfP4gbz3ocX+z+P2xv3gBWw0LPGaFl5bg9nFbXJUB9LqjWH1SS0JuwEhlIS4mysjJ8HuwQEZsjERxdXg632w0ty6Ky2AEhFkMgEEibRW/w4MHwBJ0IR4No8dWComhUVVUhFAxCzxmh4+T7ozcigYIyTsY1Hf1fEY4GOuBzW1sbijgtHDod3B4PGhoaYNTpEOJ5tPt8CPE8DBwHl8uVclzy/afAaeRxubOgQVF0lyCX8XgcWoOEMed03Hchd2ex6ODAm7C+Uv7nQlNTMyaP+wV+d94t2LDtMzQ0NGQUjpL7RjQazRh4vRDRaDRqVlGF/sy2KIoidu/eDYZhMGLEiINKZCMQCAQC4UBChKPDgKqqKsDnBdvaAo1LduPiGuox+KSJea2XKIpgWQ2euP5aAPIEn048lHV2HSgtLUVMiqpiyM+PvhQ7WjaCFyK457mrAMir0xeeMQuhUNeJv5bTIuRhIQoU+BCDkEfu+p1Tsyur4W0XzwQ/WI6zUb306QF3PyopKUFbSLYYUAQkMRaD1W5V97EaDGhJIxw5HA74w25U20eCoiiUaKoQiLi7tVzogpT2ZQqiKAI6PVr+3yWqIMnt2wf7Jx8N2ASf53nERBGmZOEoYeFwz0uvAJAFybUPPwCaTm9ZoEzc9awReq0xp4m80i+UjGqcKYa23Zmzyrndbkg0DSoeT4n3lU9qampA0zSqi4sBAEPLSrEpISYlY7PZEI3xmHrM79Dqq8foshPQ7K1BFCEUWeXsIRRFwWEr65NwpEz2WWcb4gHZAiOWwU2rPykvL0eAjyIoCDBoNGgNhnBWRQXcbjeKLGY4LLKFjNvtTiscDRokZ1O7750rEOC9KC+rgFarRTAYhEFngiHhYuv3+zPGL+oJRQCQ2I6fckU4GugU820tLajU61Bi0CEmitixYwdaWlqwu6ERKz5ai+927ERVcbHqmqagXEOrrx5uphUURUGbCMLe+X6VpHjaiTvNQI3JBnQIR1lRQBY3ypjQWVzLhpaWFhw9/hSwGi3sluJug4EmuzFGIpGDSjhiWRbRTlk+ozGx3+IVrl27Fo8//jgA4NFHH8XRRx9Yy10CgUAgEA4WiHB0GHDUUUeBpmkUffBeyvbOMYcGGoqiVFViUEkJ2n0+bK+t7/gsiYqKCgQjfhSbKsGxekiQHxyLreWYN/MB9ZgdNT8iKnSdMFGg0LjVjNLhIdR8b4G7QZ6YdBaOlMDGoqljIhgzmQdcOCouLUXbbtmSShGQeJ6HNcmKx2I0YHdd1wxmJSUlEMQoArwHZp0doagfvBDJOjUoTXda1ZcopJtXSZKken1o3AnXkEQg3IFa6VdcO/x8FOFYDO4Ir577nt9dispiB6iMUZpkAoEAKFAJ4cick3CkuKVxJrk/6kwifL4AeJ5POxmLxWKIVVdD29JSMHFV6uvrUVXsUF09hpSV4f31G7vEqRo+fDgA4L3NK1OOt9ns0GmNiPAheAIu6LSGPglHyjlL3lgFQM6C1XjtDQC6jgv9iRKHpyUYRrFeh1BUQFlZGfbv349iiwXFCeGovb0dgwcP7nL88OHDcdFFF6n96bjjjgPP84gKURj1Zpj08hjj8/l6LRwp4pCUbHGU+B4H2uKopaUZx1eWocwoj6kajQanHH0URlZV4qbpv0KI5/HYm2u6BM9X3Kb+sfYmAHLsrBvOeAQAEBNTA1rGxFgXgYCmacRjgN/JwmCNgQ8xCPu7PtqofVnqJB5L0oDFY+sJ5Xcm1wD7PM/D6/XAYZP7rMNa1q1wlOzGGA6H1UyeBwNarRbRWGq/iMaEfotXmCx0dhY9CQQCgUA4nCDC0WHAkCFDsGbNmpSJKk3TeQ8MzbIseEHAnMcW44nrr1VXpZXPkqmqqgIAtAXqMbhoNFr9ssCk1epSsu7sqduSdkLuKC6Cgd4JkyOKkae44WkKYdfnji4Tto4U8x0CjWAwwDXA1iElJSXYuGUzgA6Lo1AoBHOS0GUxGFThJBllwusKtsCss8MVlLMbZSscAUjNoialn7DTNA1EIih7abm8G8Og/VfTOz4bAJRJ+XM/bcNwmxWvbd+Nze3ydzW4tBQjKivUfeMZJog+nw96zgiaZmBgzWnbNBNtbbJ1EWeU+xyXsDxyOp1qn00mHo9DomiApgsmQO3+fftgMxpVlyIKEqKCAKfTmdJnRo8ejbfffruLFcgVV1yB7fu/R23zbvz7s5ewt34btL3TQQBATdXeNv03iCfuQypxT/eXO0o6VOEoFEI8cUOUlZXB6XSixJoqHKWDYRhceeWVKduU/mLUW2DUy8d7vV5UVlb2qo5qrJqkFPNKuvmBzCISCoXg9vpQPmoYyhPCER+JoHL4MBg4DoaEW7RZp0MogyvoLT9/AjRFg6IoUAk3xfqWvThy6Dh4Ai5E+BDCkVAX92KapsGHaWxcU4Fx57WkLAwk9xelX1Gd+i8VF9XP8omSce7IQdXYXlePSCSSNgZfOpQ+WGSR29luLkFLS+ZYa8lx3HKN6ZZvtFot+E4Z86JCrN+eZ0KhEHRGIBI8+NqKQCAQCIQDCRGODhNYli241PN6vfxw/+QNczGisgI3Tf8V1u/YhcffWtPFEkgJtNvirZWFI18ddDp9lzKB9CJHkd2BRh/AsBIM1pjqytBZOPL5fADDpASbFfUGuNO4hPUnxcXFcCYsjdrCEZiNRgSDQZj1HddsNhjSWsckC0dDHEeiPdicsr0nOosrEtK3qVarBWgaLZf/Xt3GJNppoERJJT7KHRPHY7jNgmvGjcFLW3bg84ZmRJOEmXg8jng8nvYe8Pv9MHLyJN6otcLn7WrFlQmn0wmtngKjkYUFRUDKJBzFYjHEaVk4KhSLo3379sHr86W4FAGy60tnsTHd9yoIMZw9cToGl4/EZdPm4b3PX8WWmm96XR/FUku02VULNiax0j+QLjV2ux2sRoO2UES1jCwtLYWzrQ2jR40Ap2VhMRpUMSgbFFHSbLDCZJCtPPpixaCIQ1Ky8EHTAEUNqHDU2NgIAKg0GcExDIqNBoRDIYR4Hjvq69U4YxIAf6cxSxlvqu0jwNDyI0mLtw4A8OYny3DEkLH492cvYcue7wCgi1sgy7JgtcAx5zTBYI1h5CluNO2IomGzLWUsU+59xuUC2yKPiUJZOSRRHFBBMhMulwtCLIbxI0die109WlpaMGTIkKyPBQCbuVj9v71hb8b9A4EAWE6CwFN9irE1EMRiMTz26KPw+Xw46uijwXFcF4sjXhD6bWwIhUJgdXFEI3RO8e8IBAKBQDjUyP/TEuGwRRFtyuw2dVV6Z31DymcKBoMBjqJifLj1FQhiFF/s/g+sdgvi8U5BMuOxtKvHZrMFMWdHdxd4Ou15/H4/KL0+JR99XKdDoCWz2X9/UFxcjLAgICQIaA9H4Cgqwv66Opj0HSvQJr0OfDQKQRBSBBGz2QyD3ghnQK6zK9gMnU6ftTsCRVFZWRzpdDpQsRiEsnJ1G5uYRGe7Ut5XlBXgIx126DUa6E0aVJpkKxU+aeKsrFCnEz68Xi+MnNw2Rs6CBs/WrM/f2NgIjVZUY2cJYblfZbJCkS2OKCBDvKV8EBUEzJhyqupS5PIHcMMT2cX14nke4XAIQytHQ8cZUM4ZMLTyCHz+4/u9vj5FUKaSYvTQCfdTvT69WNwfUBSFYocDzoRbD6vRwGKxyJZYJ04AAJTa7Dm5sSrB7E0GCzitDlqW65NwpFitpQhHif41kMJkfb1sARoVRYRjMRRzHHZ5vHj32+/w7rffqXHG6lpbIbGp96ByT+53bkW1fRS8kXbUe3aDAo0p46eqgmRzex3+8cqdXYL8cxwHKU6pMY4M1hi0ujg4LvU8yhhp+/wz4PPPIDEMGm6eD3QaP/OFIr5NHD0Kr/53HZqamrIWjpQ+ZDHa1P8eT2YrWb/fD51FgNCmzcnCMh+0t7fjv+vWAZCtI6dfdBH4aKpwFOnHAN/BYBCMVoSWY4hwRCAQsqK1tTVt8pqBwGq15uRhAAD33nsvtm3bhldffbWfapU7S5cuxcsvv4x1ifH/UGXmzJk46qij8Je//CXfVckKIhwR8oYyAXD5/ShNBL91+QPQsmwXiyMAGH/8eHz88Ud4fcNiSFIcJSUliAYiKfsIAg8d11W0sFqtEPmOyYEQYWAw6ruITKFQCJKGBdvS3JFiPspndK/oL4oSlhbtER6uCA9LcSlQVwdTkiCjvA4Gg7B1Ch5cVlYGV8LSqD3QhLLSsqzjwyTHngJkt7V0x5pMJkiCAMRiQGLFnuYj0Gi1A2ZxpMTqUDKpAVAzrO1ubMQR1VVo9/nQ7JInUemEB4/HCxMrC0dmnQ2++ux/7Ldv346Ql0agXau6yGhYZAx8LYqiahFSCBZHgiAgHA7jiKoqVbwdVFICjmWzeuhRJ6ymjsm82WhHPC72Ouizko2Pjnbc21QiXs9AZOpLxlFcjHavCzRFwWG3w+/3g49G1fGq1GbJyeJIaVOzQT7ebLT2SThSxbnO2ccG2BWyoaEBWobBn/73DR48bRKc4TDikoRfnTwJ5508UY0zNqKiAq54avwzZaxf/MkfceNZj+PDLf/E9uYNoCgan//wHswJMWRvwzYAXV1u9Xo9RCH1+kWBhq6TkMAwDCiahveEiQiPPjJpZ7EggkM3NjaCoigcM2wodFotGhqyt3z0+/1qVlFAdoX0B/wZxVuv1w2dWYD/IBCOlHHymOIiNIlxcByHCC/HslN+lyKCAM5sSXs8z/N46623wPM8zjrrLDVovSAICAaDsFgs3boq+nw+MKwADacpeOssAoGQf1pbW/GH3/8+ZfFyIOFYFi8sX56zeEQgZAMRjgh5oyQR96LV7cWRiYe5Vo8HxcXFaYWKP/7xFvzxj7eo75977jl88ek32N+4E+WOangCLjS110GfRnSyWq0QIh0Ph0KYhsXSNRMSz/OQhGiXFPP8AK9IK8KRJ8LDE43CkXDPMCQJR8rrUCjURTiqqCyHa29COAo2o3xQdm5qQIYAxGk2KRZMXG0totXVYIIBaJxtMKXJMNVfKNmBuCRXE0vC0mDx6n/jiKqqFPerdIKkx+2BQycHfjZxNgSCgS5WXJnQ63WwV4fV2Fmi4MH2Tyoyii5igVkcKRZb3mAwxaXIoNNltbquTDpN+o5JmznhgtXboM+KKxId7hCO6IRAmC57WX9S5HDA09oCDU3DXlSkikRldhsAoNRmww/1jVmX5/V6wdAatLjqUe4YBKPO0i/CUdpo9v1IU1MTBlvNuO64MagyG/GzQZVYvXs/7GYTRifcjAFAq2WhQ6orrNJHrjj5LlTbRmDGhOuxpfFrrPn+WTiKS7B+51p132OOORYVFRUpxxuNRgi8lBC45W0xgYLB2PVe13Ic4np9h5WkJAGxWEEIR/X19SgvKgLHsqguKVGtuLIhFApBy+pQ27wH5Y5q8EIEkiQhHA6ntf70eD3Q2kVodVTeVsWzRenjGppGXIhBp9MhLkmIxmLgEmM0LwgpiyrJ7Nq1CytXygH9Y7EYZs+eDQC47bbbsX37NkybNg033XRTxvN7fW6wujjEqNglsDuBQCB0xuv1ghcEXBMPoUoa2AXCBorBM4IBXq+XCEd9QBRFSJJUEG7shQZpEULesFqt4DgOze6O1MNN7a6sY/FYLBa4fU7c89xVuHPWYjUOxpFHHpX2XHy4Y3IhRBjYbfYu+0WjUcRNZjin/0ZNMW/YvAmWb77q/YX2AkUI8vBReHgBgxKChz7J/UKfsOpJTq2sUFpaij1b1stlhFsxsmxsTudPTYpGpc1MpliMlbz1eorIZh85Mqdz9QWe58EyDJikibI5MZm49MwzVPerLftr8PdXX08rZLS3O+GwDQcvhBFP/Mh7PB5V2OyOCB+GwRJTY2cBgIYTM67ii6IIUDQkqjCEIyUr11P/9w4AqC5FGobJKkaOsgKvWDokv+7t6rzRaARF06DDHYFo8yUc2Ww21AgCNDQFW1WR6pamWByV2Wxw/vBT1uV5vV6wrBb3PHc17py1GBqG7VXq9UKjqbEB1UYDRtpl0XB0kR0UVZM2iDFrSLUaUwRok84KjtWjlK3GHo28z4svvqA+uDEMA7vdDrfbnWKtZzKZEI8D8RgFhpUHrhhPw2bq2lc4nQ5UUr9WXg+Ua2131NXVYXCJHKNoSGlxTsKR/Bsgqb+FX/74obo93bV5vV6UVsSh1ccLPlOYkiWTpijE45J6PZFoVBWOwnw043eojGNaDZdihdfaIieNaGvr3tXU4/FAVy5C4KPduv8RCARCMlWSiKEY4Oe8PiY0/vLLL7FkyRLU19dj2LBhmD9/Po499lgAsoi/cuVKrFmzBk6nE2VlZbjoooswc+ZM9XjFvWzp0qVYuHAhduzYgaqqKsybNw+TJk1S98umLIWtW7fi4Ycfxu7du1FZWYkbbrgBkydPTtln9erVWLVqFRoaGlBUVIRp06Zh9uzZWQs/1157LfR6Pc466ywsX74cDQ0NWLZsGUpLS/H0009j48aNaG9vR0lJCc466yzMnj07xbPipJNOwvXXX49wOIy33noL8XgckydPxvz581M8HX766Sc88sgj2LdvH6qrq3HDDTekrc+nn36KZcuWYf/+/TCZTDjjjDNw/fXXq4vfGzZswNy5c7Fo0SKsWbMG33zzDcxmM+bOnYupU6fitddew8svv4xQKIQzzjgD8+fPP2CeIEQ4IuQNiqJQUV6OJ97+D1779HMAQJvHg2nTpmV1vN1uhxCL4u4rn0JV6VBcNm0eXljzEByOribrNpsNcREQoxQ0nIRomEFRqaPLfvF4HBLLqivSMW0RYolJYuf05P2J0WiEhmHg43n4eF59KG5ud2NQSYnsfpVwh0qXdrukpATuYBskSYI71JaVCJIrDofcfu6fnQmhtAyes36Oonf/g7Li4gN+rkwIggCWSf1OtAwDhqah47Sq+1WDU4451Fl4kCQJbo8HX7vfx8Th5+KbvR8AkF3NsmmzUCgEoz31wYBhxZR018nE43GApgCaKgjhSOGm6b/CmKFDVIGQoWl1wtYdimjZ5m5CmA+BoijoOWPKZ7lC0zRMFgs8SRmMmFAQBrN5wLNfWSwW+PgotAyNUqsVTqcTDE3DnoijVWK1IhAMIhwOZxV/yev1wmEtxewL/4RyRzVKbBVwu3NLvZ6MMh5pnE5V7RXKyiHF41m7ph4IWltacUxJhxBfatADkoRwNHVsCkejMDlSxwfFutIX7hDQ/BEXLGZLVg99lkR2O4GnwbCyoCREGFgrbF321en1oJNiZylxtAYydlYm6uvqMPkIWXQfXFqK9V9lH2BeEAQY9Wbc8fvFKHdU4+cnTcfzbz+kCsOd9w0GwmD1IjQ6oeCFIwUagIQO4SjMR2FNuK6Go3xG4UgZZ1lGmyI4iont3QnkkiTB6/HBMiyOWFREuyv7eGYEAoFwMOF0OrFw4ULMmTMHZrMZK1euxLx58/Cvf/0LRUVFWLJkCVatWoUrrrgC48aNw7fffotFixYhFAqlZJCNxWL429/+hosvvhhXXnklVqxYgTvuuANvv/22ulCUS1l33XUXLrnkElRWVuKtt97CbbfdhpdeegkjRowAALz++ut49NFHMWPGDNx8883Yvn07li5dCqfTibvvvjvr69+2bRtaWlpw9dVXw2QyoaysDG63GxaLBTfddBPMZjNqa2uxbNkytLe3489//nPK8W+88QbGjRuHv/71r6ipqcETTzwBh8OB6667DoAcr2/evHkYMWIE7r//fvj9fixcuLCLdf9nn32G22+/HWeddRauvfZaNDQ04KmnnkJtbS2eeOKJlH0XLlyI888/H9OnT8fbb7+Ne++9F3v27MGePXtw++23o7GxEYsWLUJVVRV+//vfZ90W3UGEI0Jeufqaa7B582b1PUVROP3007M6tjghUFhMdjUwb0QIorz8iC77KhY80TADDReDyGu7uHcBidXNDBOubCbSBwqKomA2mdAe4RFOyhjz5xUv4Ynrr01xv0o3OXA4HIjGeHjD7YhEQ6rIkw1y7IiULZDSLGPYbDYwGg1Ay1noYtoisIIwoOaxoiiC6STmaWg5pbcnyeLFnXjdOUB4IBCAJMVx/nGzUW0bgV+Nm4N/rL0lY4yizkQiEWijNPxOeeXbXCwgLsUzWxzF45AoOcZRIQhHysS81GZLcSmKxcWsXPWUSdej/5wvl8ewWDjvlZTPeoPNZkNT0o8pnSaO10BgNpvh53noNAxMJhOcTieKrVZVsCm2yqJFe3u7mvmxO3w+H2zmYgytlMcoq9mBpoY9va6fIqQ53v0/AOgI+DyAInc8Hofb40HRoA4XsiIdh7gkod3rw476elXsdgcCKO9k9afT6WDQG+ENd0zKPWEnHI7sBGjlnhbCDHQmWRgQo1rY0liUGvT61KDrBSIcCYKA5pYWDJ58MgBgcGkJvD4f/H5/VlZ2giBAy3JqvyqxVwJA2jhXilCkNYhg9ULBiyHq727iN0n5rpJFye4sjhSxiKHZlDFXSHz33Y1ToVAI0agArUFETKDgajr4rQMJBAIhHT6fDw888ABOOOEEAMD48eNxwQUXYNWqVbjkkkvw+uuv45JLLsE111wDAJg0aRKCwSBeeuklzJw5U7WGEQQBc+fOxamnngoAqKqqwkUXXYQvv/wSU6dOhcfjyamsWbNm4YILLlD3mzFjBpYvX44FCxZAFEU8//zzOOuss3Dbbbep+wDAM888g1mzZqXNcJwOv9+P5cuXp3i9OBwOzJs3T30/duxY6PV63HPPPZg/f37K747D4cC9994LADj55JOxbds2fPLJJ6pwtGrVKgDA448/rv6udy4fAJYtW4ajjz4a999/v7rNYrHgL3/5CzZs2IAJEyao288++2z84Q9/AACMGTMG69atw4cffog333xTfYbfsGED1q5de2gJR/v27cN9992HDRs2QK/X45e//CVuvfXWHs3HL7/8cnz77bddtr/77ruqEkkobMaNG4dx48b16tjyctkqqM3dhBJ7BSRJgtPTgsrKyi77Km5V0TADgy2GaJjpkp0HUAJDpxeIBnIFH5AnrS1B2epCMTF85KrZqvtVfVs7bl/2QtoHX2UVv969O+V9NsjiWcd7igKkeNc2oWkadocDHp9XORC0b2D9qkVRBAVgt9ur1pVOBJ52+5OEI38ABoOhi6mm4iY0rHgMOFaPIY6jQYHKWjjiI1E0bTOjaZsZFC3htD/UIezToIlKn4VPdlVjCsZVTflx3tPYhONHjUS7z4cQzyMQys6CRpmU3TFrMThWB4qiQNNMyme9obioCIzb0xGkPuCHI8392t8YjUbE4nF4IhEYjUY0NjaiKCk2miIcuVyurIQjr8cLk77j/jAbrPD6eh9jRhH+XOdOhVCaeNiRJCAeHzDf/GAwCDEeR0SMYbfbC4oChlotiEsSdjc2Yc5ji1Wxu8XtwUmWrhahJcUlcIda1ffuUCtKq7OzklQXBSIdQpkQZtIKjWajEXSShWa+gq53prm5GfF4HIMSVo7K//r6ehx1VFfX686IogiG6fi+ldfp7kFlzNPqRWgNItrbC1s46owqHPEdAmCY5zOOV8o4y9BMqnAUk383uxOOFNdUrVGEGKPg92Uf/45AIBAOJkwmkyoaAfIcZMKECdi8eTM2b96MWCyGs88+O+WYc845B2vWrMGOHTswfvx4APLcYOLEieo+gwYNAsuyaG2Vf+NzKQsAfvazn6mvGYbBlClT8PnnspdKTU0NPB5P2rKefvpp/Pjjj1kLRyNHjuwSKkWSJKxatQpr1qxBU1NTiodHQ0NDitZw0kknpRw7bNgwfPLJJ+r7zZs3Y8KECSmLQZMmTUp5/giFQti5c2cXF7azzjoL99xzD3744YcU4ejEE09UX5tMJtjtdowfPz7lN2rw4MHYuHFjVm2QDXkXjnw+H6644gpUVlZi8eLFcLlceOCBB+DxePDII4/0ePzxxx+P22+/PWVbNg/w/cWuXbvwj0WL5AltQmiQJAkjRozArfPn561ehyKlpaWgKRqtrgYcPfx4+IMehCPBtN+/Momo+8mCsC+ESFBKO7GgaRpULAa2pVndRiVEk4FawVcwmoxoaZXjMCiDwBHVHdmvDInscelWlZVra/LuB4C0Ilkm5L7b8Z6igHgGMa2irAy1iYkvHQ5DikazjlF1oOBjMdz83y8AyNZG90+ZCAmAMymQqcvvR1GaNlBW3806GwB5cmHUWbIWjjSsBmWj3Sgb1WEdY7AKKClOP+kV43GA1RRMVjWO48AwDJ5//0OMHzkixZKts3VWOpQxbnD5SOg5WYTy+nvveqVQ7HCA27lLDVKv9XhQXD2mz+XmiiKshYUYjEYj2lpbwTI0QjyPdp8P7Yk+lm2AYb/fj9KyjhhgRoMFfr+v1yKiMi6IFmtHwOfEeDBQmQ2VAOvLN+8AsAMamsbqC38BhqKgYRgsveVGDCopwU2//hWuePixtGNRaXkpXA0t6nt3qAUjsozLZrPZQFFANCQLlpIERILpxXKzyQTG1yHq0rzsTplv4aixUQ6wXlUsW4ZWJ/43NTVlLxzRHY9z3Ym3inDEGURo9SKa9hR23B7F4oiCnO2zw1WNVz8P83zaxAfJ0FSH+60kSRAEAQzNQIj2LBzpjDHEYxQkSbYuVBatCAQC4VAh3ZyoqKgIdXV1qhV9Z+8F5X1y4gCO47qI6xqNRvWOyKUsjUajuqMn16m9vT1l32zK6ol0zyarVq3C4sWLcdlll2HChAmwWCxqzKXO3h6dY6gmXzMg/3YoWT0zndfv90OSpC7Xo9FoYLVau1xPZ4tklmW71INl2bSeKb0l78LRqlWr4PP5sGbNGvVBj2EY3Hrrrbj22mt7tByyWCy9tljpD7Zu3Yo9e/fijHFjYdbrEYlG8cWWbdhfU4M/3nrrgFutHMqwLIuysjLsa9yOiZEzsK9xOwBZXe2M7K9aipa6Vrjq5JXJoUOHdtmPYRjQAT/KXloOQHb98PzsTFADLBoBgMFowr7QfgAdk0CG7ug/iotWukmnMtC2+utS3meDHMup4zwULSEuxlPSHytUVlSA+1EODswkBKSBFI4YhoGGYfDgFNk0laKAcEyeLDl9He5iTq8vrbueIhBZdEnp5HW2rON+aDQMtPo4zMUdkw+ayez6IooiJI6GRNMFIRwBQElxMY6tqlQt2WpaWvGnF1Zk5d6oWLWIYsf1x+LydfVlVd5ut4OmKLRc/nvE7EXQxIScrOYOFMnfo16vR119Pdrb27G7oVEV2Wgq+8xUfn8AxqEd96JJb0E8HkcwGOxx4psOxYWVShKPldcDlSlMeSC5YfyxGG6zqKKzhqbhCQZUF8iowYCYKKbtV+XlZVi/Sx5HJEmCK9iS9Tii0WhgNpsQDXkAyIGx42J64chkMkGT7KqWiMPVm+x/B5Lm5mawGg2KE+O0QaeD1WRCS0tLD0fKxGKxFOFIk7A4SmdN43K5QFEAq4+DM4iIhPmMQbQLCQpy31Duk10NjThqyGA0tbcjLkkZ668KTxSjvlYWW/ScEVEh8wN1S0sLKEq2OIqLcsdubW0lwhGBQDjkSPfc63K54HA41DmEy+VK8SpQBJxc5hi5lBWLxeDz+VK2KXXqXFYyvalXuvn52rVrMWXKFNXdDJC9pHqDw+FImwwleaHabDaDoqgu+8ViMXi93pyup78Y+NlwJz777DOcfPLJKQ955557LrRaLT799NM81qx3hMNhcP+fvfMOc6M62/49XV0rbfPuuuGKe6eZYjAOScBAIBAgdOzEdPxiyBtD6DUQioOxY9NMLy8xEPKFEiDUUIwX2xhjXHHdri5Nn++P0YykXWlXW6U187uuvVYaTTlzZubMOfd5CsPgg2/W42fTpiIQjSGW7Jx2J+aHRXaGDBmCj9b+E5ffcxIeev6PAJBV0SUIAk8++RTefPNNvPnmm/jnP/+J8ePHt1mPYRiA41B/3oWoP+9CNJxzHghFAV0A03Sn04kWXp9VNYSj9IbN+Jgt9pIxEGqO7s/4ng+yLCNdJyOSn7MJHRUVFaCTghEd6nvhiKZpqKqGET4vRvi8GF7iNYOe7mtqwl0vvIS7X3gZ73+zDqVZgnaHQiHQFAOOTg3aXVz+whHDMOaAwkBTyZzWHoqi6DGhyOJwVQOA6poa8JIEB8dhUHk5bMmy53MdDXFi255N4IU46pr3YMfeTRm/dQW/3w8k4pAqKqGxLIhYrFNWcz1F+myOy+UCQ1OYM22KKbKt/J+r4HI48s4gF41F4OzBDHSmcJQ2+DUyhfV1ivkat9N8BgFd2I7EExCSFh0NyWeqLMtzOGDAADRH66BpGqJCEILEd2pwXlpaCiGmW9kIScujbMdxuVyg4nEw9XUgRBF0snPWFdGuJ2lqaoLf48aWffsQFwTsbmyE3+0yLV46QhRFMHSqzTE+Z+tzNDc3w+aEKYgYy4oV4/1GkQQ0aKaY++g//omte/fh4dVvAMh9DVMWSymMerHbXO32yxobG2FzAiQJcMn4WYa7hYWFhcWBRDQaxZo1azK+f/311xg3bhzGjRsHmqbx3nvvZWzz73//G3a7HQcffHDex+nsvv7zn/+YnxVFwccff4xx43QL9CFDhsDn8+Hf//53m30RBNFtwxJBENpMgr711ltd2te4cePw9ddfZ/T3Pv/884zg2A6HA6NGjWpzPh988AEURSkKQ5mCWxxt27YNp59+esYylmUxePBgbNvWcdDQL7/8EpMnT4aiKJg0aRKuvvrqDJ+/vkYURbgdDjxyyQW6ef5pp+DjDd9i+Zv/giAIfeY+8FPhsssvw3GzjzO/V1ZWguM4030iHYIgOszKxLIsCA0ptw8Atp07CnLd0q0djOOnS0RmzNAsKjlFUbBxdgTijWAZtlPWH7Isg0yrJoJMzdK2jptSUVEBLR4HIUmgwiGwHNenKdNZloXYStASkt/HHHwwdkX1+2DsmDFt0ncCuhmr0+bJqEMn60U4zVqpo+PHwxQUiYAQ1/8rEp3T4kiWZWikbnGUzcWwEFRXV+PNN9/EJ99uBAC8+fmXIAkiL+HIGKw99Pz/4o8XLcEbHz2Djdu+yvitK/h8PkCW9Rg0JAlNFAsiHA0bNgy33347ZFnGtGnTIAgiBpeXm+6iAOCy29tkxciGLMsQBMEUi4CUcJTP9tkwrCyINHcbI+BzX4khRpuwKxzBsBIPAryAuCSbAm59MIDBFRWoTwpH2bIVDhgwAKIsIMIH0BKrM5flS3l5Bb780g0+zCHSpJcnm2WTy+UCYlHTBdLx3begGabP3ZBbEwgEoKlqRjwoUZKyzk5mg+cFsExKKGQZ/b7IlnGzubkZrENvIzlHSjjKNw5EX2PGKCJIqKoGlmVBkSTOPOYojKipxnmzj8M327Z3fL+ntfGGlZydcyIq5BbNGhoawLn0Z4uiNXAOwhKOLCws8mIvQSFLXpneP2YX8Xg8uOOOO/C73/0OLpcLTz/9NADgrLPOQklJCc4880w899xzYBgGEydOxJo1a7B69WrMnz+/UwkmOrMvhmHw5JNPQhRFVFdX49VXX0VDQwMuuOACAPpY55JLLsH9998Pn8+HI488Eps3b8bKlStx0kknZY152xkOOeQQvPTSS3jllVcwaNAgvP3229izZ0+X9nXWWWfh//7v/3DNNdfg/PPPRyQSwcqVK9tYEc2bNw/XX389brzxRpx44olmVrUZM2ZkxDcqFAUXjlqboBl4PJ4Ozf9nzJiBU045BUOHDkVDQwMef/xxXHTRRXjmmWcyAmt1BkIQ4Fi+vMP11AEDILUSvJhXX8Xk99+Hd88ejPv0v/p5ANDq6hCuq4Pnscdgt9shz5gBJS1wGAQB3GOP5TwWSZKAzQY7z4M/9VRoVansNeTWrWDefrvjE2NZCPPnZyyi338f1KZNHZ/r8OGQfv7zzN099RSIPAY70rHHQh071vxONDeDTUaW7wjh/POBNBGC+uYb0J9+mrHOkOSfAblpE7BlC6hf/SpjPeaNN0Du3t3hMYfv3w9CyRzQX/TVl/A1NnR4X0hz50JNc5Mjd+0C849/dHhMABDSzCABgP7kE/z8u+9QWlcHjmUx8d//xjl1dbD9399BJ0UkhyDg2EAANE1nCGLsCy+AaGnBuQ314PldYDk2a9nVo44CjjkmtW0kAu7ppzHhq69A7N2Pwf/WRZdos4SpdTIcf/sbbEkrBvGss6CVlpqDu3Hfb8LsTz9BWTQC59/+lvM8NacTYqvI/sxbb4HMQyRWxoyBfNxxGcsmfPghfrNvH7iPPwNlxNsJhnFOXR0u+sUvTMFNOuEEqCNSsWWI/fvB/v3vGP/ZZ/Ds34eqD5eav/1y97eI7mg060yYNw+U4SpIUaC+/BL0V7o4MnfLNsRjcdQEBAT2coiHGEy0N+JgfAPba6+1aSNm/vgjDq+rBx2NYFSgJec91dk2Ih3xtNM61Uacsm0bPHV1qL3nPrySNBsePWoUWJbtsI0YEQzinLo6jDr6bFQOGIFzf3k1Nmz9Es//awkGv/UW8pEusrURk957D+fU1SHyj9egESQ8dXWY/MEHcPzwQ8a2+bQR2dD8fohnn52xLFcbcbTx4ZtvcPK2bRg6OHOA7eBYTPngAzg6sCjlBQFVggAbl4qn4wvr9Ve6ahUcHQgl2doIat06nNfUhPgH74HfsR2kKIIKh+Coq8Pw11+H45NPoA4aBCmZkcTAaCM6Qp45E0r6DFeyjUinhudxTl0dgm/VQRo+BFvrm7A3GoOttBQCw6CuRReO+O++x3lNTRj48sttjjOluRnn1NXh+7/NQpwigfJy1NTUmG0T89ZboHfsMN+Dra31rpIV/OByoY4tw5aZB6G0tBSVlZUgSRLcypVAUig4/LvvIO/Zg8jU6VC/XgNbKARfJAzH8uU524h8EObNA9IsvNLbiPYw+hGRSARDKytx58UXYPiGjbiFpvBFSwDRzz/Pq43g+QRcpA2lq/8KAHBL+jWpfvFFOL74ImO7KR98gB0+fcbTEJDk776D47PPOj7RAvQjKhsbMTweB0uRySDgFKoYBod/sw6eeBzDkm3QiDfeyHoOzKhUllWCIMBu2ADPO+/gnLo6VAgkQvFm2Jcta3NvaX4/6ur3g3WmrPmOjzRh1JtvwtFB3Axl0iTIrSYquKVLc6ydSU/3I6h16zrcrjNtRHp/VFXVvNqIXBj9CHPf330H5oMPOtyup/sR6W1Ee3S2jUivq8TFF3erjUiHefVVkHV1ObZI0Zf9CJMuthEkSQITJoBqlVW5kGMN4vLLgS5MWHm9XnAMg+VSYSxZOYbJK0Zla8rKynD55Zfjr3/9K/bu3Ythw4bh4YcfNidhrrzySng8Hrz++utYtWoVBgwYgKuvvhpnt+pL5UO++6JpGrfffjvuu+8+bNu2DdXV1bjnnnswcuRIc50zzjgDFEXhxRdfxOrVq+H3+3Huuedi3rx5nS5Xay655BIEAgGsWLECAHDsscfi2muvxbXXXtvpfZWVleHBBx/EAw88gMWLF6OmpgaLFi3C0lbvhqOPPhr33HMPHn/8cVx33XVwuVz4+c9/jiuuuKLb59MTFFw4ykW2eCqtueqqqzK+z5o1CyeddBIeffRRrFy5skvHJQgCjnwtAbI0KA5ZhkOW0bzfCMBJgBUEOBUFNlGEI+kKlbGtIJgBTXMiirABsLndmds6HB1vCwAUBUfr8jJM3tu2OVdNy29buz1zW0nKbzsAjpISIF1UtNny2zYabStGkmRe23o4DpAzLVjcPA8P0PF94XRmnmtzc/7n2rp+WRYzJ0zAtJEjwTAM9uzZA6eiALGYXocAwAuwqSq8Xm+mNUby2ngJAoQswmXjspc9KYaYdUVRgCzDJghwaQpsCb0DrcoKnIoCjufhSA7kHB4P4POZMcjYSBjuaBQDbLb260nT4Gx9rsnjdgjDtLkPS2gaTkWBEk+AMQQwnodLVZPnn9yvw5G5bTQKyDKYeBwelQDNpyyMXIoKQUyY5+Hw+cwOn8fj0T8nfxvodqNZDcBDSXBWyVArCDRsdcFP03ABbcrLShIoigYlJ9uDXOfdlTYiiaOTbcTkYcNwcHU1VIbBvIULAeguWgzDdNhGUAwDp6LAARI2zoEBnANbd38LAPCzLOgsln9tyNJGaMnrinAYIEg4FQWlNN2mvrrcRmS7D/NoIzhBQCISw+Y9e0CAwKiBNaApGpwQ7rB9UOJxUJoGG5uaTeMo/TzpeLzD7bO1EZBluFQVtp07EHO5we3ZDTqo+8uXkKS+T5Lsevtts2Vum+VZtZEkXIqCsaV+lBMEjirzI+Z141+8BJIksb9FL08gEMjZPtg9Hhw3fToEQYBos2HMeedlJjmgKH1gl3wPtmZ0VRVGV1UBU6cCrQbAUBSzzD6KglOWoRKAJoqwSyLKWFYvU442Ih/S2wgAGW1Eh/h8iEaj8CStnoJNTRhAEHAqMhLxRNb6UlUVwVgMkiiitLQU0UgMXo1BpH4HQBDwOEty31fhEGwDkinqGQ00Q0CMxfLr9xSgH8EJAmhNg52mISsKfD4fvC4XEAkDsRjUSARORUnd761wG27amu6+6bHZoPE8nIoCDwgIoghbtntL09DU1AhuQGqfTkqAEgx0XFcs2/Zc870fergfkde2nW0j0usqjzYiZ3mT/QgTu73r7Xc3+hHpbUT7Be5CG2H03bvZRrQhn237sB9h0p02IpFo23cv5rFGDioqKvDEU0/lHfuwp/F6vZ3ObnzTTTeZn7NZ5wO6uHfRRRfhoosuyrmf+fPnY34r4RDIdDfryr5WrVrVXvFx2mmn4bTTTmt3nfZYtmxZ1uUOhyOjbgy+aDUh0/o7AJx77rk499xzM5ZNmTIFzzzzTMaybPU9a9asjGxyrZk2bVrWY7722mttluW6Jl2l4MKRx+PJGvU8Eol0GBi7NQ6HA8cccwzezkcVz4GmaUjkkcZYBSC1yrzEAIjTNIKahhe+1P1EKZLELw6ZjhhFIcEwIGgasiBASd9WEMC1c0ySJGGz2cDzPPhIBFratmQ8DiaftMsUBaFVeWlJApXPuSpKm3NlCQJEHttKiQTUtG2JcBhsnmmihWBQf6EnoXi+wxTTJEnC5nIhHA5nxORhVBVkPuUlSUDOtByIUjScLIt4B9tLsVjGuZKxWH7XBmh7bUQRVHLgLgLgWRYxikKDKMFOEAAI2Ow28CQJnuczgqsZ10bgbIjFY2A4LmvZVVGEC0jVVSQCjqYRBpBgSPB2fSAjiCRiFIUwYF5zMRyGxjDm9VAFAbymgfB42q0njSAgtn5uFCWva6NIEuRW2yp2O2IUhf2ygmqbDQlZRoOqQeQ4JNLc86R4PPM+jETA0jRaZBkxikaTyoMgCHjtZYjTDIK8ap6HEAiAcjhSbZUgmOdNlPgQi9VBdCb089M0hDUKmtuNKNq2EVFNA8XZQEkiIgSRs64620akI3aljXC5AJY1xXrDB7vDNoKmwbMswrIAwwEpGGmG2+WBxHF5ZXLI1kZoHg9iFIU4SUEjCYDS67R1fXWljQBy3Id5tBFRksSadeuxdsdOMBSF9+67G5qmIkqSHbYPYYKAQhAZLkWMzaE/W+3cCwZZ2wiahup2I8DZkCgrA+nxgG5sgH37NshOJ+I0DVVVu9x+yzyfeR8m24jWaG43RJYB5bDDCUBICFD5EHxeL+qSwlF9NIphPl/O8xyZNMHWnE6Is2ZltGmMooBmWfM9mCs+WLY2gqMowAji7nQiRlGI0gw0ux0qQUBN1lOuNiIfhEAg05ogrY1oD6MfUV9Xh+9aWvD5pu/xy0AAN/78Z9gViyMuSVnr67PPPsPzr7yCDS4Xjj32WOyv24cdoU34Z0MDSJLCmXMWgOc4BBSlzfaNogDKodcfQQCcQ8O+xkbEs2TUaUMB+hERgoBMEHAxDBRFQVNTE1i7HdFYFHA6EQ+FEaMo835vTTRpraBBgyAICPM8IgShb2N3IxzU36+t7y0VQHNTCw4anmpfVCfQ0JzI/j7VNHz6ySdI8DzEhgbMSbf0APJuv3u8H5HPtelEG5HeH1VVNe82IhtGP8LcdyKR17n2dD8ivY1oj862Eel1lehmG5EOA+R1rn3ejwC63EaQJAmb3d6m717IsYYNXQ8EXFFR0WnxxsKiP1Bw4Wj48OFtYhmJoohdu3a1iX2UD9kCBXdqe45DfMGC/FZuFVtFOfVUfB0I4ENJxB0XngdATyG7IxjE8zt3Ye7FFwNG5yx9W5pu95gURcHm8yERCOgNatq2ykEHQepqeY85BmhlFprvtonzzstvu9bblpRAzre8rbZVJkyAOGFCu6sbdaUYdWVse+KJeR2u6c03oX3yScayVePGYmIijrn5lDv9mDU1Xb82hx8OHH64+X3vunV4vrYWz+/8EQDAUBSevG4hPtiwEb+kqIxzTZx5JgDg7Y2bsGXrDxh20PCsZTfcQBRF0bd3OBBfsAD/icfxFV2PScfrAlq4gUVtYACOPPNMMAcdlFFmhmHA2mzY6nKhqboKv549G4decknnznXOHGDOnA4qKPu2ofPOw/P//S+eF2Tcc+gYvPT9VtTSMVQefDB+0/qc07etqIC8YAGeXrMGe4W9QOhTUCSDB37+//DkWxvQYCvDr9O3T26rKAqUadMgJge539ls+Mf/C2JGsq6EGIXPfxyAUWefjcGHHtqmvO+UlmL/tBlg6+twqI3DMe3dH51oI9rbtrfbiH9/8SUmDaiBIfO3hBpQWVkB/vzz888c16qNUK+8Ei+8/TZapk6FRpLwNzbg7Kuuyi46dLKNyHpMdNxGqKqK519/HRf+7HhcOn6snh4cAEvT+Gj6dBzaQR3v3roV+z/8MEM40qpH4PkBA1BzwgkY0Mp9osPyJtuID7dsxdcAAr+cCwBwrquFPxHHb6+4AooRu6d1+51sI/IifdtkG9Gaz374AT9EQxgzYzIAoHZvHUJfrMWYykrUBXR3l7UAYnPmYFYn21FAbyPavAfz3DZ+8cXm5+YtW/D811+j/sijIQ0YgMpoBHOGD8OxRpmytBF5k34fprUR+Wzn8XgwtqYa5885DgQIyANrsIsg8NWOnTgzSxn+FQhgc0sYXtaNxsYmECAxc8YpGDltLgiCQHPVSLy540OoRxyBEWkznoqi4InXX8cgWwiuZFw2gpaxjaK63u/p5X7E5s8/x7Y1a3AcpwsMsVgMis+H98vLcORvzsDGL7/C87v34OzLL4eapX2Q/quHDTCysokTJmC3zYbnP/4Yx8+YjX9/EcRv58+Hrbw8494KhUJQnnoqGbdOr6vPhrgR0HxZ36eNjY248Q09UDe++grHynKG1XyX2+8e7Ed0ZttcbUTW5zCPNiKf4yqjR0MaPbpL5e1OPyK9jejUth20Ee323TvZRmR8PfXUrpW3iPsRufruhRxrcEWQwcqi+yiK0q42kI+Aa5Gi4LV19NFHY9myZQgEAqa7zbvvvgtRFHFMvp2RJPF4HB9++CEm5Dtw6AUYhoGiqmYKYgDY3dho/mZR3DAMo79AVBVGajFClsEyhQ1qbsTqufX836K6rBQECAiSbkqb675iGLrd33PB8zxIOm32hdbM5dnweL1ojsdBxONd8qvuDoZZ8/wJYzCsxIMFk8fhifWb0OLK74VfVloGu1iGU6f83uzkj6magZYd+/La3u/3g4/p1tQEkcrolCuVvSSK0GgaGk1nDVzbH6moqEBzMJU2vDlUhwFV3UtXTZIkHC4XgokENJKEzeEs+MvduD8qSkoy2neCIDp0qwZSmZwaWvYB0LcZPECPl9GdQOlejxtUfaP5neR52JzOPg34XDNoEH74cq8ZmH57KAy7zYaagQOxb+sWAEB9SwBHFXgG1mgvSF63EKQFvk+D+efCZrPBbbdl3Fccw4Jls2fGE0UJIyomwW3zISTtAi/wqCofgqHVqXg+Tru7TZKISER3yd29zovSQTx+rPUg2kwjGMy0ECgmjPeOPxkIPh6Pw+lyIZIUJKMJHhzH5WwftFQWCfOzYQlpZDjMZhlpWLzt+MoHb6WIH2s9COy1A+CzJoow2vPYhIlwblgPWZatPp+FhYVFgTn99NOx3wwf05ZsLl8WuSm4cHTWWWfh2WefxWWXXYbLLrsMzc3NuOeeezB37twMV7XFixfjtddew3fffQcAWLNmDR5//HHMmTMH1dXVaGhowJNPPonGxkY8/PDDhTodcBxnph82EJIDBiMDjkXxYnT0CEWBZgpHCjhn/hkDegNDOBrg95uDi2+T1ke50m7TNJP837nHnOd5EGnCEUmr5vJseDwekLEYNEHIGui+NzGO52Bo2GkadhcNDYDP789re5ZlobIsBvlTgfYcrBtkelq5digtLYWqAhJPgrWrEJMpwf1Zjq+qKmRJgkYz0GgGfLxrmbSKjQFVA/DDtzvN702hOoyZeli39+tyu0EmEtAoCi63q+MNehmCIEASBBQ1czZVVtW8RBpjFnXZ/90KAKApBiv/9C4IgszfMisLHrcbzK5UUG8ykdCzh/UhQ4cOxTvvvINfv55yEx8zejTKy8vxzZqvEOd5RBOJrBnV+hKjXshkW0Ym+D6vq2xwHIdgVI+dNai8HM3hMOoDgZxtuyLLoEgWFElDkiSIogCOyexfsAzXps02Ym6MOqoZrlIRI44IYMeXGgLB/LK3FYJoNAqSIOC363URj8fhdDqxf78u7kcTCbiczpzbG2KRYSEIpIQiRzvCkRFCYezsRrOumnfx2P6FD+FwuE0bb+xDtel9hWxpnC0sLCws+pb777/fnLiz6D4FF448Hg9WrVqFO+64A1deeSVsNhtOOukkLFq0KGM9VVUzOtfl5eUQRREPPPAAgsEg7HY7pkyZgltvvRUTJ07s69MwsdvtiPN8RnDvOC+AZZgOU8FbFB5DoIEs6wH9ABCKDK7AHUBjACGkdXCNz7mFIyrjf74k+LhpZQTkYXHkdoNu0C0e+noQxjAMXE4HAkKqXoKShKH5xOsAQNEUeDmO3S1bUOEeiBDfjJZYHUiyYwsSQM+SAABinAJrVyHEKZAUiZIsxzdmpDWGgcYw4A8Qi6MBAwbg0491dxBVVdEY2N/tFKiAHqCbFHhoJFkUg3tAv9/EVsHzpU5aFlx+5m0oKxlgvh8MF5qu4na7QaU9myTP97mA+/Of/xx+vz8j9tDIkSOxfv16tITCqAsEAaDgwpHdbtcFQCFZX0ViceRyu7Htu+8w/4EleOSKS7Hq3ffw1eYfcmaHVRQFFEmCIikoQjLQNZXZnaNIuo0lm2Fx5KkQQDEaHF4Z9hIJkX0RFCuRSAQujoU7afUbiUTgdDoRS97z0QQPpyN39iLjnqRIyvycj3BkWGt5K1N1JZaJ5m+thSOjfTeEo3ziu1lYWFhY9C4j0jIhWnSfggtHAHDQQQfh8ccfb3ede+65B/fcc4/5fciQIR1uUwicTicUVQUvSrBzekcnxvNwtjMjZlE8GMIRocgwhnKkLKcEpQJhWKvxaap5ItkxzWXJZgiVnbU4SsTjINOFI0b/nMu1yuVwgI7qA49C3Oe+Eh+CfKpsAUHElDxTqLIsi+boDtz/zmW4avaDeGfjs/i+7muU+svy2t5wSRNiFFylEoQYBZ/Pm9UCxRDeTOEokcjrGMVOVVUVovEwrnvoN5AUCZIkZmbE6iIelwtkOAKNIOEpEuGIZVnTgtSAF6Wc4m06hlBUXT4E1eVDzOVa2m9dwe12g+BT9xLJJ1BS0rcuo3a7Patr+b59+6BqGrbs3QsgtwtnX0GSJGwOB0heABQFmigWxbu5pKQELMNg5f9chUHl5bjmtFNw89PP5XT9VRQFFEHp1mpJC7jGwH7wQhzBaAt4IQ5VU9vcV0bQe4ZLCXw0qyIeyyP7YYGIRCLwcBw8yRhHoVAILpcL0YTensZ4Hs522gdTOCLoNsKR0+7J+J6OIRwZ7z/9s759IkvbbQpH9uS7OsdEi4WFhYWFRX+lKISjAwljZjySiJvCUTgeL5oZc4v2MV3V0qwKKFUtGuEokWZZw3cgHNHJGEdUJy2OeD4BypcaWBgiUrbOMqAPGsnkgKQQ7pg+vx+BQBMA3XIjEE+Y8dI6wm63w865sGjmzahwD8Tp067AvzY8jXp5C6LRqDljn2tw6fP5QBAEhFgy21ycQllpdtHJqD8t6R4nHCADi+nTp+Oss84yB182mw0zZszo9sDJ5XSCamzSLY7K8nM97G3sdhsSrQRUXhTzuu/NYPRprm6qqkLT1G5Zo7pcLmg8bwbaogUhlYK8wBjP4Za9ultRNhfOvsbucIAQBZCGeFAEwlFpaSkC0ajphuwoL0eMF0yLxtaoqgaaIEESlGnd/MaHqzBu2DS88dEz2LjtKwwaMLyNoGmKIWxKDKFZDYmEkGElXUwEAgF4WQYulgVJEAiFQnA6nYgmxdJoItHuNTTEIpriTKv1lHCkW5tlmxQxXBtiARpEch6ApDJjJKVjLFMsi6MOeemll7Bq1SpQNA1ZknDyKafg0s4EMrawsLCwKAiWcNTDGC4CoVgcFUl3lXA83ueuAxZdw7Q4SjPxJ4rA4shu1zujfFpn1BCRclk7GIPRzg5KBUGAg0oNLAgCoOjcFkc2m82sr0IIR/7SUjTVJeNdSBJkVc17gOp0OiEpghnjqIIZCJpiwBA0zjjjDHM9j9uDd959p832FEXB63VDjAcBAGKcQdnQ7AGAjUGbynLQWA5CMp1xXwYx7g2cTicuuOAC8ztFUbDb7d0WjhwOB2hJhEZSRTG4BwC73YE4n/kcxHkejnZcZQwMUXp33VaUlwxAMNqCaCyY8VtXcLvdgKqCEEVoHFc0AZ8BmC6bO+rqwXFcUcT5czidIAUBRLI9y+fa9TYVFRUIx2KICwIcHAdFVdEYDOZM56xpqhmzR9M0uFxuTB89C4MHjMC5v7wavBDHX1+6oc2EVTweB0GkBBBAt6LRND1VfTFcn9YEAwGUsAwogoDXZkMgEEB5eTl4QYSsKIjyPDw5xHogFXieTROOBEEAQRCwt+OqZqxb+0YVAIAgNUw/fX/Gb+mkLI7sGd8t2rJlyxZomobGo45ByYcfYPOWLYUuUtESDAZRX59KPuFyuVBTU1PAEllYWPyUsYSjHsYwLQ8kLTAAIBCJwttOx8aieDBEmHThqBhc1SiKAkPTiKd1RhOiCI7jcgoPM2fORCgUxpFHHtmpYwmiCBeTGXOFYoicHeH0uilEPfl8PmxJimhBXjSX5YPH40GUD2UsiwkhME59IH/h3EX4fuc3+HzDv3Oev8/nA5/QXXFkns0pWhnCkcaxUDkO0DQkOpgt/ynjcDhAihI0UjGF00LjdDnRHI6YQYybgqG8XZGNtmXl6rvwx4uWmJYhQPcE1/SAzwrHgeSForFwNSZMdjc0wuspDjHLaVoc6c9zMdxbAwboWQjPu/d+VPp8CMVikBXFXN4a3biMAAE9U1h5WRk0TYONc2AA54CqKghFWtq4BoqiCIoGos0poTI9hl0xCkfNTU0YnCyXz8ahpaUFQ4cOBaC7qcV4AVXtPH+SJIEAAZa2QUwmLhEEASzDgaVZ83supp6aOxtPOmYMu2RZi8niaOPGjWhp0QOgT548ueDCcjyRQHzkKMSmTgfT0oJ4EQdnLzQ33XILtmzenLHsueeeKwrrTQsLi58elnDUwxgzrMFImnAUi2HkcCs4V3/AGNwxDfWQ/X5QsSiIpEBTaGw2W6bFkSjC3k5Hf9asWZg1a1anjyOKUsaMNABQVO7Odbq1RCGyyJSUlJgxjgLJMuYrHJWUlCAuRKCoMihSbw6jYhDeMn0wOWPsMXDaPfh8w79zDgT8pWXY2ahbdYmJ7IGxASAW07Oo6RZH+oDFyBBk0RaO40BIIgiSKpoBrcvlxrYd280gxk+89U5yecdCjWHZcvYJl5uWIXXNu/Hw83/s1vkZ9w8pClAAQCiOTGGAfg0ZhkFdIIBhBx1U6OIAAJx2O8hwBETSFakYhKMRI0bglFNOMcXlKgCTbTaMHz8+6/p6MHXCdC0bUDUADUmrSwBoCTVAURVUVVVlbCcIAjQNWPtayopm/Bw9sUGxZp1paWmBb9hgAICfY9Hc1GQ+SzGeR0xo3+JPFEXQNAuG4iAKAXMZQ7Ngk5nosr3bDEtdl18yXdViATrjt9bHAUHokwIoHuGI5/mMZDNnn302zj///AKWSBeOjHegyrI53eB7G03TTKvfYnTTBIC6+nrER45C5IgjwdTXw//WP9HY2GgJR+2gKIqeBKGfW3NbWBQjlnDUw9hsNthtNrREU1lKWiKRnINJi+LCEIj87/wLDX4/PJ9/BsRjRSEccRxnBsQGAF7IL7ZKZxFFCWKCRKRJF4HcZRIISss5sEgPvt3ZQNw9gc/nQ0wUISmKKSDl+7wZna9wogU+p+4WEuZbUOUYDgCgKAZ0MluRLMtZBwy+Eh+27WGhaYCY0HIe2whMq3Ic1OR1i0ajBc80VazYbDZAkgBSKRrhyO12I2y3m0GMz5tzHNZu3dYp4cjl8JqWIaKku/N1R+gxhCNCEABVhSYIReF+ZeB0OBAMheAoEoHUbrOBbG4xhaNiuLdYlsWCLsV4IQANGDRoEDZtfM9curfxRwD68nQURQHDApNOSlnRyII+uGqdga0Y4HkesUQCZcmA06U2DlsbG9OEIwGxRMfCEUtzYCkOoaRlKs/z4FgbOCaZrTSLcGRYz6oqAYrUJ1JUhcj4rfVxCJqGRjM591kIjPfOn357Np58598IhUIdbNH7xOIJaD793asxTMECiS95+GG89fbbGDliOJb89ZGClKE9NE1DPBqFMHUGpPIKqJz+HBjZES3a8vTTT+OFF14AZ7PhqSeftMZeBwCRSAQvvvgiZs+ejWHDhuW93ddff40NGzbgwgsv7LWySZKE5cuX49tvv8X3338Pnufx9ttvt7nvbrvtNmzatAkvvPBCr5Wlr7CEo16gpKQEgaTFkaqqCEaieVtAWBQWY/Y5ePQsSBWVCM6egwFPP1kUs9KtLY7yDcrbGVRVhaZq2LPeiz3rvSBIDUdfvBsEilc4MhrokCAiJIqgaTrvgbPhyhFKNMHnrICqKgjHW+B0TgSgp3CmSF0syiUceTweSDwNWSChaciZCSkWi4FgGICmzQ6gYYVk0RaO46BJEgiSLLirqIHL5UJCFFNBjJPXMR/XD5vNBpZhEU2EzWXRuP65OzHwjHudFAQQkpixrBiw2+26cFQEbSigXwdKlk3hqBgmBbrL0KFDEQg1IRoPweXwYk/9NjgdTlRWVprBoQF9IEqQBNxlqbY8sI8zfys2mpr0pAelhnBkt+HLffXm/R0XBMQ6iDGWSCR04YjmTIFCd1WzgWWTsQOzCBfGu1WRCNOdT5XJjN/SEUURoGloyWQUxWJxZFixVfpKUOb1mN8LCc/z0JLWyRrDFCxRxP79uoBat7+uIMfvCJ7nociymanPmHAKh8PtbfaTZu/evdBoGgLPIxAIFEw4amhoKJhI6/V6c8bH649EIhE89thjGDZsWKeEo7Vr1+K5557rVeGI53m8/vrrGDNmDCZPnozPP/+8145VLFjCUS/g8/vRkpwRCMcTUFTVEo76CUaHUHU4oLEsZMYHyHJxCkdSz7vQGbPOQ6YGUTo4LcU3rWUNCApkmu13JztUVzED0osiQoIIr9udt9m5Ye0TTOgDlIgQhKIqKfcfkgSZFI7SB2Ctjy8JBKTkzH0uESEajQK2zA5gNC0WmkUmLMsCsgyNJItmcO/xeBBOG3iFk2nM8xV+PF4vIsmA2AAQiemuM7nExnwwBs2EKIIUik84Mq6drQjaUEC/r0hFMePYFcu91R1GjNBd4a998ExUlNZgT912TJk8pWjdb/Ilm3AUDEdMITkcj0OS5Q6FI452gKMdiAf155XneXCMDTRFg6LorMKR8c5XRBKw622/LBEZv6WTEo6Y1PciwHADc3AcnBxXFMKRIPBQTeGIhSSKBcnqZ4ilGopPNAVSlkVqMlOfxjAARVn9hnaQZRmqzQYqGi3YM9jQ0ICLL7oEglgYQZRjbXjiyccPKPGoWHG73Xj33XdBEATefPNNSziy6Bo+nw8tTXrcAENAsoSj/gHDMKAZxkzVTIgCoGlFMRDjOA6ClHInEESpxwc9RkfK7pEzZqUJMrdwlO5HXgifcmPQHRElREQR3k5Yb7hcLrAsh0Bcf16Dyf+G6xBBkCCSAS5yCUculwsSr0HiOxaO1FbCkWVynhvTykhVCxI7KxsejwfhaMwc5ITiMXN5Pvh8PoSiqUCwoWgLWJaDw+HIeX91hNEGkJJoWhwVg/uVgRk3rkiuIcuyIGQZhCKb3/sfmQPdmpoaXHDBBQgEdCFyGjEBP//5z9tsRZK6VWS2XbUW/UVRxGuvvZbhcjVs2DDMnDmz+8XPEyOgc5AX4LNx0DQNmqaZQk9zSLe8aG9iJx6Pw844YWOcpmiSSCRgY/V3uo21Z42xY7wDZDH1TpOTbXw211JRFKHRNECSAEkWjXBknLPDxsHOsRnCd6EQBcG0OFIZBijirH6FxLBI1ox+HkGAsNksS+V2ECUJKsOCQuHcb0OhEASRx3mH/S8qPYP79Nj14V145vN7EAqFOi0cbd++HUuWLMHGjRshCAIqKytx8sknY/z48ViwYAH+/ve/mxn9rrvuOnz00Ud49tlnMXKknpX45ptvRnNzMx55RHf7FEURjz32GN5++200NzejpqYGF198MU444YSM427YsAHLli3Dxo0bQVEUZs6ciYULF8Lv92Pfvn341a9+BQBYvHixuc3q1atRXV2d81xWrlyJxx57DABw6KGHAgCmTp2KZcuWAQBqa2vx6KOPYvPmzeA4DkcccQSuuuoq0wvBOO5NN92E2tpavP/++6AoCieeeCKuuOKKDO+Krgjeqqri3nvvxfvvv4+HHnoI48aN6/Q+CoUlHPUCPp8Pm3bsAAAEkgNDy8+2/2BzOMxUzWTyfzEEm2U5DoKU6owKkgTW1rOz+IZwxEcpKBIBIa7/T/+tNYUWjgyhJiJKiIoSXL7SDrZIQRAEysvKEYg1AAACcf1/SjgiOnwpuFwuaBogxOiMbVsTiUSgJF2bQFEgGMaaOWyHQgddz4bH44GiqojyPNx2O8KxOFiGyXvAU1rqx+Yf1mHXfj399LfbvkJZaVm3ZtpJkgTDcSBEEYRYPAGfDZikMFMsAg3DMCAURf8jiIJYSXYXTQMI6H8adBHzrLPOMn+nKAo+n88UkgwYhoHaSv834va0djPeuHEjnnzySYCmQTgc0MJhcHZ7nwtHNEniDx99jnuOPgz//nEPgNSA2piYa+/5i0SisNFO2FknYnFd9I3H4ynhiLNntcIxxOBwAwtHiQQhTiHawoCisltAiqIILRkPj6Dpogk2bohsdpaDneXAhwv7ztE0LUM40hi9zizhqC3GfamyqftN4yzhqD1ESTIDrxc6blulZzAG+UcWtAydYdGiRfD5fLjxxhvhdDqxZ88eNDQ0YNy4ceA4DmvXrkVNTQ00TcO6devAcRxqa2tN4eibb77B3Llzzf0tXrwY69atw7x58zB06FB89tlnuPnmm+F2u3HEEUcA0EWjSy+9FEcccQTuvPNOJBIJLF++HIsWLcITTzyBsrIy3HvvvfjDH/6ASy+9FNOmTQMAlJW1n6n85JNPRkNDA95++20sXboUQCoe5KZNm3DllVdi8uTJuPPOOxEOh7F06VJcfvnlWLVqVUb7vmzZMhxyyCG466678P3332PFihVgGAaXX355l+tZlmXcdttt+Oqrr7Bs2TLTYri/YAlHvUBJSQkCyQGh8d+yOOo/uFwukMnOliEcFUPmK5ZlIUZSpq+CLPfaYGznGh9KBoj4sdaDwF47POXZrY2ATLW9EMKR3W4HSRCIihKikgyXu3PxYsoryhFMWggGYg3gWE7P6JU8L+Pscglnxr0hRPUBaC7rtHAkAil9wGG3WxZH7ZA+kC0m4QjQXdTcdjuCsVin4hNVV1fj888/x81/m28uM2bDugNns4GQJJBJYbmYhCM6KcwUIv5ZNiiKAhQFUBSQRVKmzqJbvJF6Fq9OWKpxHAdFzmzHVJkwf0vHcBPbc8U1AE3DsXED/P/6JwRB6DP3vkAggFKHHf87YzJq3E5cPGEMrv/wvwiFQuBY1uxftSc4RCMRONhKOFkPNE1FPB5HIp6Ak6sEANg5Z7vC0db/+uEqlcx3YYkvuyu0JEm6xREAUFTRCEeGxZiNZWHn2IIFojaQZRmaqpp1VUyufU888QQ2f/99xrJTTj3VHOT2NcZ9qXGpfl4hs9D1B0RRgprsFxfLM9gfCAaD2Lt3LxYuXIijjjoKADB9+nTz97Fjx6K2thZz587F9u3bEY1Gccopp2Dt2rU488wzsW/fPtTV1WHKlCkAgDVr1uDjjz/Gww8/jMMOOwyA3tdpbGzEypUrzWdq6dKlGDNmDO69916zXR0+fDjOOeccfPrpp5g5cyZGjRoFQE/2MGHChLzOp7KyEhUVFSBJss02Tz31FHw+Hx588EGzbzl48GDMmzcP7777Lk466SRz3ZqaGtx0000AgMMOOww8z+OFF17Aeeed16XYlKIo4oYbbsDmzZvxt7/9DYMH961FWk9g5SrsBbxeL0LRKDRNQzAa69SMtEXhcbvcpnBE8N3PetRTsCwLMW0GRewF4chouIdODcBVKmLEEQFMPXU/NC13atP05YWIqUGSJBx2O+KyhJisdFrkq6goR5DXLY2CiSaUlelxjzQN2LnvB4iy3qHNNUAzhaMYDYLIPWhvbgkAmh6Lhg60QLUsjtqlGIUjwy0ymLxu4Vi8U66R559/Ph5++GHz75FHHsFdd93V7XJxHKe7X8nF535lXMdiseyhaRqEqoBQ1aIpU2dRVRUkQYIkyE4JRzabDYoMaGmbKMmAz63brVAoBILjgOT1Uxx6OxcMBrtX+E4QCoXgt3EY4fPCTtMY5fOCSC632WxmEpL2+lehUBhOzg0Hq1umhsNhxGJx2Dn9fGycI6sFh81mA8vSqBkfNt+FZUNjKCnJHo9MkiRoxv1E00UhhAAp4YhjaHAMA7HA2d6MejEEI0NAKoYsdG+8/jo2btyISoZGJUNj/YYN+PjjjwtWHkPkU5lUe64wjCUctYMoiUVjcdSf8Hq9qKqqwqOPPop//vOfqK+vz/h98uTJWLt2LQDdzWv06NGYOXMmvvnmGwC6tRHDMBg/fjwA4IsvvoDH48H06dMhy7L5N336dGzevBmKooDneaxfvx7HHXccFEUx1xk8eDDKysqwadOmXjnXb775Bsccc0xGv3LChAmoqqoyz8dg1qxZGd+PPfZY8DyPbdu2dfq4giDg2muvxfbt27FixYp+KRoBlsVRr5DuzmDMSPf3IJU/JbweN8gW3cSf5PUXdD5Zk3obhmEQToszJMlyjw+ojUEe51JBMRocXv3FS2i5B37p93ah7nO73Y64JCPeQaDUbJSVleGLxNcAgGC8AeUV5cnBmIZbV/wO5/7yagC5LY6M4wkxChzH5RTYGurrYI9GwTTUw/P5Z1AlybI4aof0e7tYBvhmIPbkQDMUi8HTicDWHMeZs2eAfl4ej6eNS1FnYVkOhCQVZaYwMnntiuUaUhQFqCqgqmbg+/6GqqggCBIkQXVKODImQIL7OXgqRAhxCrEWBixLt7k+kUgEmj3VlqpJYSkSiaCysrIHzqJjGhsbQQHYGgiBIIDhJV44WAbhcBgcxyKYfA7bE47C4TBcJV64OP05DYVCiMaicNTodeHgXIhGs7v+lPhKQJIt5rtQ0wiUlmZ3kZAkCWqyDrUiclUTBAEMTYMkSbAMA6EDQWvRtddh43ff4pZbbukRa8jWpIQjOuN/IYS2lFUxAVEUIYgi/njWmfjFIbqlhSjLBcuMBaTENC3tXajQdFGIbMWKJElQkyJ3sTyD/QGCIPDwww9j+fLluO+++5BIJDB69GgsXLgQU6ZMwdSpU/Hkk0+ivr4etbW1mDx5MiZPnoxQKIQdO3agtrYWY8eONfseoVAI4XA4p2tzU1MTCIKAoih46KGH8NBDD7VZp7V41VNEIhEzllE6fr+/TcbC1t5CxnfDIrczBAIB1NfX49e//jUGDBjQ6e2LBUs46gUMkSEciyMcjxeF6GCRP16PB0wyTSuZ4AGCKAqLI4ZhIKXNoEiK0uPCEUmSIIhU3AsDVSVzHqsYhCOH3Y6ErCDRhQx4paWlCMcDUFQFIb4Zw0qroKoqKJLGjfMeRSyZPj3XAC1dOLI7cg9gNIJAZNIUSBWVCM6eA+977yJiWRzlJH0gWyxuTkZbHkq6EARiMXhrBhaySAAAjmNByFJRZgozrmMh3FizQVEUoGlJi6PiKFNnkWQJFEeDIulOzaob9+/6f1Vi8kn1pvuVz9/WSjMWi0GlKDD1dZB9fv1dCPRpVq69e/aguaUFCz/4FDRJYvWpP4edYRCJRMBxNlPAzXW/q6qKcCQMl60ELlsJAN1iKhIJQxAT4IU4CIJCoKU56/a+Eh+CifTg2Cz8/uwx9ERRhGK0WUXkqiZJEphk+8nmIWjt3LkTAFBX1zsp6lsLRygG4YggTOtfrzMllnqdDuypb+zzchmIoggQhB5wPYlGUR2Kfz9lZFmGxliual1hyJAhuPvuuyHLMtavX49ly5bh2muvxZtvvomJEyeCpmmsXbsW69atw/XXXw+Xy4URI0agtrYWtbW1mD17trkvj8djuoNlw+/3Q5ZlEASBCy+8EMccc0ybdbqTbbY9PB6PmXghnZaWFgwbNixjWetJPeN7RzGWsjFgwADMmzcPf/rTn1BSUoKLLrqo0/soBvpnr6nIMYP1JuKIxhOWcNTPcLvdoJOdZCqRgN3hKIrZcoqiIKVZHMmK0uMDaoIgkgFUWwlHSm53ofTOV6Hg7HbwsgxBVrokHGmaiggfQCjRjLKyMmiaBoqiMLR6FGysvr9cwpFxvEgjB3uOmW9N0yAkEpBLy6CxLGSfH6rThZBlcZST9Hu7GJ4/QHcBczjsCEYNi6N4r3VuOkPKVU0Cks9wsWC0C8UiHJEkqVscaZppDdXfEAQBcTECaFqnBkfGvTrqqKYM96ts97AgCIAooPKZp8A01MP9xWcA0Kcxcmwch6MHVuHBY2fi/lmHAwDcLIN4PA6O4xCO6SJWLuEoHA5D01S4bT7T4igQCCCRSODfX/4du+q2YnfdVtTVZxdJSkvLICVS7ZCUoHMmOhHTXNVUqngsjtKFI4aiIMntl8sQU7ua5TGf8gAw68oIKF4Q1z6jz0LAtDRwp1ksu+2OgrqTS5IEUFSqnNDrq1jcIIsRKS04drE8g/0NmqYxdepUnH/++YjFYmhqaoLNZsPo0aPxxhtvoKmpCZMnTwYATJkyBe+++y52795txjcCgBkzZiAQCICmaYwZM6bNH8MwsNvtmDBhAnbs2JF1HSNrmtGf6ex9zzBM1m0mTZqEDz/8MGPS5dtvv8X+/fvN8zL4z3/+k/H9gw8+gM1mw/DhwztVFoPZs2fjpptuwooVK/D88893aR+FpjimcQ8wDAuE73ftQSgeh9PnL3CJLDqDx+MBkXRRI/kEXEUi/DEM0+vCEQCwHGsGTDVQJCKnO4ApHBVwYGiz2SAkouBludPWFn6//nyG+WaEEy3w+XyQZdl0Y6GSHVtFyR4g3Ol04vzzz0dTU5Pp390aURShyDI0W6psqo1DrL57LkoHMuliUTEJIV63xxSOgrFolwIk9jQ2jgMRi4OQZdAMU1Su0cUqHBGqWjRl6izhcAh19e/j6FG/gijm77ZiiB40q2W6XyXjuqUjiiJkjweBU0+H7PMjdMyxsD33dJ8OxkRRRFWZDyN8KWHLQdPgeR4sm0otnyumlzE7nBCjkBUJTs5jWtKcOWcBBg8YgSmjZ+LrHz7Iun1JiQ/yFn3fmgYI8dyJTvSsaoYYUjwWR4qigE6KQTRFQZZzJ7oAAFXN7pLdU5j1YriqJeusEPVFGm0TQZoCkTtt4sntsBc0g5ksyyBaidsaRULq4Br+lDGD1BfRM9gf2LJlC5YsWYLjjz8eNTU1iMViWLVqFaqqqlBTUwNAF4meffZZjBgxwuz3TJkyBS+99BIoisoIQn3ooYfiqKOOwjXXXINzzz0XI0eORCKRwPbt27Fnzx7ccMMNAIArr7wSl19+OW644QbMmTMHbrcbDQ0N+PLLL3HSSSdh2rRpKC0thdvtxjvvvIPq6mqwLIsRI0Z02C8cOnQoFEXBiy++iIkTJ8LpdGLIkCG48MILMX/+fCxcuBC/+c1vEA6H8eijj+Kggw7CnDlzMvaxd+9e3HbbbfjZz36G77//Hs888wzOOuusjH7fZ599hkQiYcZk+vjjj+FwOHDQQQe1sWACgJ///OcQRRF33XUXWJbFr3/96y5cscJhCUe9gCEcPfDqagyvrsKQInBlsMgfj8cDLZHQZ6QT8aIYGAL6QFpJmwXsLeHIZuMgS5kDT1nSclryFMPgi7PZkJAVSIrS6UD0xmCqIbwXsiLB5/Nh//79oCkjqK/+csrVCSEIAmeffXa7x8iaVpflrLS67VBZWYnS8nJomtZnMVXywVNSgm937sT2/XUIhCNFYXFk5zgQoTAIWQbLFo+bGpASAItFzDJc1aBpRdF2dQWSpHDkiLkod1VDVmQ9WHYe5+LxeEDTFMR4ajAqJziUZYnboygKNJaDVKnHYpCTMSH6MuCsKIpgWrkTsiQJQRDAsqz5PswlHBnuCM9/eT/K3DWwsw40NuquR8NqxsDGOTCgbBCitbGsMex8Pp9ZV6pMQJGR0+JISLM4KjbhiEpOgtCUHkxdz8qX/Xk06qHPLI5o/X8hAhkbk10EkXpHO9P6D06bDfF4vN366k0URYFGEKa7KBWLguQFyHT/tJTsCyRRF46IIngG68O7+s0xS0tL4ff7sWrVKjQ2NsLpdGLy5Mm45ZZbzHe4IRylWxZNmTIFBEFg1KhRbRLT3H333Vi1ahVeffVV1NXVweVyYdiwYRlZyyZOnIgVK1ZgxYoVuP322yFJEioqKjBjxgwMHKiPnUmSxI033ohly5bhiiuugCiKWL16tWmRlIsjjzwSp59+OlatWoVAIIApU6Zg2bJlGDNmDP7617/i0UcfxR//+EfYbDYcccQRuOqqq9pMPC9YsABr167FH//4R1AUhdNPPx2XXnppxjp//vOfsT8Z3gQA7rjjDgDAvHnzsgpHAHDyySdDEATcf//94DgOc+fObfdciglLOOoFjAH2JT//Gf6zfoOVUa2f4fF49BlpQQCZSOScYexraJrOmC2Uld7JCuR0OiGJqc66qgCKnDvNfDEMCDmOQ3Oyk9DZjFLGQKAuvBOA7s6xa9cuMEk/eSYpHHUnIKUhEKlpLyWV48D3YbyQ/kZJSQmeffrpQhejDeXl5fjkk09w4X0PAAAqKioKXCL9nqeSWdUYtniss4DU+7CzQet7C5IkoRmuav1UOJIkEeXugXBy+qSGIAh5uegSBIESnxf1W+OwefSBeqiByhooVFEUqOkuMgRpLu8rFEUB0+oa0QQBWZZx2OGHIxaLYeDAgTmvo2FxdPXsh1BTMgw+e4UZ1NTl8Jr/ZVnKmqmqpKQEQkKDpgFigjKXZUOSJGg2/R5XSbLgg1YDXTjS68eoJ7WdjIKGcJQrGUR3MYWjpJhl/C+EcESabvakef0daVbBDo6DqmkQBCFrP/5vf/sbfvzxRxx33HE4/vjje7x8mqYBsozKZ55Cw1m/hefzz2DbuQNaWoIFi0xkOSngFjCzodfrBcfa8Mzn9xTk+Bxr6/SElt/vx6233truOkceeSS++OKLjGUlJSX4/PPPs67PMAzmzZuHefPmtbvfMWPG5IyFZDBr1qw2Gc46gqZpXH/99bj++uvb/DZlyhSsXLmyw33Y7XbcdNNNuOmmm3Ku89prr3W4n2zbn3HGGTjjjDM63LbYsISjXoBlWRAEAZ/bBbELFhAWhcWISUXyCTACD6+3OC2OFFXpFeHI5XShkU8LCJoUkXIFCC+GwRfHcYhKXUtFzjAM7DYH6sO7AejCoSAIYGku+bv+vzvCkdEp1dLKprIcREHI21rAoji46qqr8Ktf/QqAfu901de9J+E4DpSigOiCq2ZvM3/+fJxwwglFUU9Asr1KBscu1HP38ssv4+1//T8AgM3uwC233ory8rbuYtlQFAWCKICj7eBoXahIJBJ5x3Y7aOgwfPVVC759OyV4Dh06tM16sqJkBOU1PvepcKSqaD0vQRK68HHKKafglFNOaXf7QCAAO+vEsPJxAIASRwXqApsBAC6H/l53O1Kxj1qLm16vF5oKyAIJMRkkuz3hCE79fawUgbWDgf5+yYxD2J4olE04evHFl/DWW29h9uzjcOGFF3arPIZAZFgcoYCuakSaoGbE7uLS3F9snP6+5nk+az/eGDBSFNUrwhEAgGFQf/a5kH1+PanGh+/3mqh3ICBLkh43q4CxoCoqKvDEk48XLCOf1+stigktiwMTSzjqBQiCAMswECUZoiR3eiBrUVhM4SjBg+b5osioBiSDY8sSNu/Zg0Hl5ZDk3hGOPJ4S7A+nuTIIxS8cMQyDWBctjgD9mq/f8wmA5CyzIIBNCkZc8n93gsKawhGTKpuWtAzheb5orDEsOsbtdmPs2LGFLkYGLMuawbGL7X3jdDoxZsyYQhfDxAyOXUDhqHbtWuyrq8eRNVX4ZMcO7Nq1K2/hyHCpsbNO2BndNSAWi5mx2jriT3+6KWNAQ1FUVqtaWZahZRGOesuFKRvdtWZtaWmBx546N7fNj8379CDILrsuHBmWR8FgsE07bNSLmCAh8XlYHKW5qhVT5itVUbF5zx5TtGnvGmYTjr6prUV9fR3Wrq3tMeEISRdEo84KYnFkuqoRkCQJFEmCTutTcR0E5SUIApqmQVV675kgCCLlLsr6ododIIS+C1Dfn1BVFaqiQKMpaHRhxduKigpLvOlFVFVttx2jKKoovCEORCzhqJegaRqSLEOS5aIK7GrRMekWRwTPF01WPJqmEecFzH9gCR654lKIktQrMY48Hg8UIXXPSknro1yxnkzz9z6ciW5Nd4Wj665fhO3bt8Pn88Hn8yGRSIBLZlMz/mdzZcgXQ3RS09oCjbGEI4uegeM4kPV1cNXXgbPcGNrFjLmk9I7wng8Cz+P4IQNx0fiD8cne/Z0SpQ23Vzvjgp3VxfzOZH5iGCavVMJtLI6SacH7coBPkgSUVsGaFVUzs4R1RDAYhJtLCWoemw/xWAwOu8tMemAIR6FQqE3MDEMkkngKYoIEQeR+D6YLR6BoiEVkcRTjecx/YAnOP352h+trmpr8n6p3YwAuS92/9m0sjgpgyWZgDCyppGshTVPmxFxzOIy6ZIysbAKEpmlmHSm9JKYSBKHHY8s4cHGEByhGUvGzaGhFlNnQoue544478M9//jPn748++iimTZvW5f1XV1e3ccuz0LGEo16Cpmkoqgq5gJ1Ti66REo54aIlE0QhHFEWBJEms/J+rMKi8HBq0XrI48pizqwDMz7k6zKNHj8bUadOyujv0FSzLQkjGf+qKUDt+/PiMjGjxeBx2Vp/NZ2gWNMV0K5C14eampQtHdNdSjFpYtOaQQw7B1m3boGoajpo5s9DFKWpSwpHcK8J7Pgg8D5YiwSbL0hk32EgkAgBwsC44ksKRsawnyRBCkhAU1afCEUMzkFoNyiVNgyPPNj7QEoCbS7c48kFWZPjtqXeZYXmUza3EiBMi8brFkdPlzPnONVOnI5n5qogGrS6HHX9ZMA9b9uwF0IGrWvJ/+my+lLzmstz9czLvn2Rso0IIkgamxVHy+KqqmRNzq959D19t/iGzzGmk109vWeFlF45UM2aVRSZmX4qmoBUwxpFF7zNv3rx24wMNHjy4D0vz08ISjnoJkiShJE3pLOGof2Gz2UBSFKhoBFCUonFVo2kamqZhdDLTgKpqvTLwKSkpgZhIzWhJfPszrVVVVbgzmUWgUKSLRT3hqhOLxWFLG3A4bM5Ozeq3pnUmmfTPVufGortMmjQJkyZNKnQx+gWmcCTJBXs3S5IEhmHNjGGdERnCYd3Vysl54WD1Nrk3hCNRkqA5WrWlfRy7h6Zp1MXiSMgyAryAuCSDVxRU5BnHKxgMYbBtqPndxSUDYqcJRxxrA0MzZr2m43K5QFIkxAQFKUHC206GVVmS9TTggD5o5btuodrT0BSF0QMHYle9nlGuXeFIbWtxZFga9cS1NyyL6KZGSJUDQMWihReOki5nHMPg0asuw6Dyclxz2in4dsePuPvFl7PWV7pYpPWScGTEY8tA00AQlnCUjXSLI7WI4oxZ9DzV1dUdZlWz6B0s4aiXIEkSatKU1TIr7V8QBAHOZoPtx50Acsf26WsM90dA79RJcu/MmJeUlECWNESbGVC0hlA9B4fTUdQCaLpY1BOuodFoFANLBpnfHXZX1oFFvhgdGKap0TTN15KuElbnxsKi7zDaTKaxAXQeLlu9gSTLoEkbKIIAmcwSli+GZYyT84ClOXCMrVeCsCYSCRAUA6a+Ti9z5QBoFNWnQrckS3h/VzN+NnQQXvp+K2obmjDE68k74Ug4HIZzYCq7kDMpHNltme90h92dtX0nCAIupwPbvvBBUwiMG587w6osSyDjcRCiCEhSt2LidYfXX38de/fuxeDBg3HSSSd1OpCymnRVy7A4Sr6jeuJdZdzrFS89b2YKg6YVVDiiSBKapltwGxNzjvJyhGN6PLFsdWgsIwmy1yyOKIrS47GlQagq6CLLnFksGG2TRuvCkTUpZ2HR81jCUS9BIOkDDcsfuT9SWlqKxM4dAJB30NHexnB/VFUVarLT0hvCkRHQ7+vVVeayYcMre/w4Pcm4ceMw7KChcDldPRKQMBqJwlmVclF02FzdsjgyZlkrn9PTy2sUhYbfng+gMEFBLSx+qjidSRfU5ia4hw4pSBkURQadtBqgO2ltEQqFwNAcOFqPveayeXtFOAoFg3Du3Qvn5k3QKAp7F14HkGSfDsYqKiox3MZiWIkHCyaPQ1yScccXa81r2BHRaARONq0dZ90gCAIOW6b7eXsWpcfNno3a2rUAgCNnHpXzWKIgwP31V0iMHAXbnj0Iq4WJ+bd8+XIAuuhgCEeEmXa+4+0NESRdDBEl/ZqLYvvCkaqqeOSRRyCKIq6++uqskzjGu7D+nPMhl5UhOHsOBjz3dEFjHJEkaVodpaO1Wi8do35IgoKq9k6WM5Ik21ozqZrlqpYDUziiKEs4srDoJSzhyMIiC3fefjvq6urAcVzRpJE2OmFyMnZW+rKeZNy4cXjwwQczXrpVVVXtbFF4Jk6ciKWPLuux/UVjUTht6cKRu1uDM1VVAYJA/bkXmMu0ZGe0L7MUWVj81Jk0aRIeeOABSJKEQYMGdbxBL6AoKqhkinSSIDrVBoRCIbhtJeZ3F1eCYDDYwyUEaIZBdMIkxCZPSS1k2T4djPlLSyFHgrDTNOwuvbsaE6W84g6KoghJlmBjUiKTnXWBIEjYucxkBHbOmTOG3e9/t6DDY2maBmgaQocfAamiEvxBw+DftbPD7XqLoR439sYTZtkM2YPo4J2THvA5QzgSRNg5JySp/Wsfj8fxr3/9CwBw3nnnobKy7YSTIRBJVVUAQUBm/SBouiDCkWFFTVKk7iXQql6MusgmHKUsjqhec1XLanGkWeEvcpFucaTRdFFlNrSwOFCwhKNeIt3SqLOmwhaFpxhTaRoikShJZhaP3hCOCILAwQcf3OP77S8oioJYLAqnPTU4cdo8CIUCXd6nqqp6fJBkWl1Aj/EAWO2DhUVfQpIkxowZU9AyqKoK0rR2IDo1aA4EAhnCkZv1IRAI9nAJdUtIpcSX0WapNNOpQN7dxev1YruYssYSFQW8LOeMt5dOPK67GdmYlEhko+2ABghiAjv3/QCCIDCkaiQYiutW1kxjwKr4/NBYForbXRAhxBA+6DQRREuLidNRnzRXwGdBFOBylCAQbsi7LLmOIcuy7q6dLsYQhY5xRIKiKMitrMTMeExZLLtNi6NkWIregKIoPcaRpqXqS1VBW8JRVlpnVbOEIwuLnscSjnoJTVVBAFnNXy0suoIRx0eSlV61OPqpY8w8O9MCqDrtbjQ1/1ioIllYWBxAZFiBoHOu7MFgEG42FWvHZStBc2B7D5ZOR5YkaHTmAFWj+zbFdUlJCUJpg7+gIJrLO8IQuELxJuxu2QKCIDDQNwKapuKLb9/HF9++D5pisPJP7wJAt2ISpQ9Y9f8U5ALErjPEDCZNzNA0DaRp3UZmrNeadLHL+KxpGgSBR5Xfi8bAvm4LPIqimHH+DDSSLIjQZghHJKkLR4rSKoNfO8KRaY0Estf6+Eb50oUjQu2dbLoHAuZzSNPQaMoSjiwsegFLOOolVE0DQZKdNkO3sMiFIRIJaRZHXJ7ZZSzyx4h1kW5x5LJ7EI50PTh29uwo+j8rBpqFxU8LDa1iqXRi4BloCaLUNsz87rb5sK0h2FNFM1FkGVqrAb7ax9mvfD4fAgnejNMT5AVzeUcYg8j/W/sIAIAiGTxw5v8DCALHTDkRs6afbLa9ds7VLUEs3UXG+C8VYNBqWsiQhOl2piiKGROHSmbxyyXSpPdVTZcySYKqqvA49TrPV2DL9V5TVRVE6xg9BRKO0mMcMQwDSZYzYkIZyUiyTdCZrmokBfTS3HCGcGSUWVNTyy0yMN1oaQoaRUOMdT0upYWFRXYs4aiX0DQNJEFYFkcWPYYhEqULRz2Ret4iEyO1tTMt8053g2OTJJk1VoH5m4WFxU8KY1zdWd04FAphSFmJ+d3FlSAU7vng2JqqAq3TfvfxRJjf74ekKIhKEtwsi+YEby7vCEOIOOeQa1FdMjwVIBoE/N4KDK0eZa7L0AwUOd7lcmYTjlRF0UWbPrQOMfqahlCkqipkWTZdm2iyfeEoXRQ0rrPhwud1lwJIuQBmI59JkGwWR8gSX6gvSLmqEWZfSpRlcEZYgHaEIwMCRBshuKfLh6SrOwDAytSck1RwbN3iqJAZaxsaGnolaUE+eL3eogu10R0ikQhefPFFzJ49G8OGDet4A4texRKOeglVVUEkU+1aFkcWPYEhHPGSaJpUWxZHPY/RMU5P2Wy3OZFIxLs8K2rGKlDVVKc5eQ17IzOehYVFEaMBQErI6MzkUjgcgqsmlWLexXkhigJ4ns87TX0ni5ixrC8nwsrKygAAzQkebpZFC8+Dpih4vd4OtkyVs9IzGIP8I83ler8sU7jo7gSf4RaXLhwBurVOXwpHZoyjtCDYsiyDMYSj5P9cVmPp7zdjHeN96PeUZ3zvKmaMozS0PrZkM0h3VcsmHAlJ4SFbPysVHLv3XNVSYmdahjdYk025SAm4SYujAglHDQ0NuPjiSyAIXXd/7Q4cZ8MTTzx+wIhHkUgEjz32GIYNG2YJR0WANWLpJUyLI9KyOLLoGUyLI1EyYxz15EDBQscMqsrazWV2Ts/Mk0gkYLfbs27XHuaMZdpsK6FacaosLH6KqKqK5gSPRNI1Jl9EUQQv8BDkhBm3x8npsdgikUiPvg+yudcSmtang1ZDOGpK8Bjq9aAxwcPv8+VVBjODmNZ24q71Mi0ZWqCrZLM4AnRBqS/f0SmLo1QQbFEUTeGINSxpcrjRZROOjJh/fm9FxveOyGUVU0wxjkxhhiDM/tV3P/6IEpcLBAhTOGrPVa03vQrMOrTGEHmREWuMogriLgroVqGCwON3p92AqrIhfXrs/U0/YsXf70QoFDpghKP+Sk9P5hQLlnDUS6hayuLIEo4segJDsOBF0Ypx1IsYMRzqm/egOdSgdyqTIlJXhSNjNpNQFGjJTiiR7Jhb7oYWFj8tREnCP7f/iKMGVkHthJuL4S77r2+fxr++fRoUyeDq2Q8A0IWj8vLyHisjRdMgWg3mSVXtU6Hb7/eDJAg0JV3UmhN83udoWHLuD/2ImpLhCPHNEKQEAA1q6+xZqgxbNyyDUhZHTMb/XAJNb2Fm+kqzOBIFwbSg4ZiUoJWNdNee1sJRmbcy43s20sWizghHhY5xRJCk2Ze6bsUTAACGorBg7olgWTbruaT69USvxTiyxg6dQ5Ik3feXJKHRNGSp763Y0qkqG5LhElvsbN++HUuWLMHGjRshCAIqKytx8sknY/z48ViwYAH+/ve/o6amBgBw3XXX4aOPPsKzzz6LkSN1i86bb74Zzc3NeOQRPa6cKIp47LHH8Pbbb6O5uRk1NTW4+OKLccIJJ2Qcd8OGDVi2bBk2btwIiqIwc+ZMLFy4EH6/H/v27cOvfvUrAMDixYvNbVavXo3q6up2z6e+vh4PP/ww1q5di1gshtLSUhxzzDFYuHAhAGDlypV47rnn8Oijj+K+++7D1q1bUV1djSuvvBJHHnmkuZ9PPvkEL730ErZs2QJBEHDQQQdh/vz5OPzww8113nzzTdx+++147LHHsGLFCqxfvx4nnngirr/+erzxxht4/vnnsW/fPnAch6FDh2LhwoUYO3YsAP05f+655/Daa6+hrq4O5eXlOPPMM3H22Wd36Tr2NpZw1IsQROczplhY5MJQruOCCEW1LI56C6NTfecTVwAAaIrBtef+OeO3zmIKR5IELXnNCFnK+M3CwuKnAU3TOGFwNYaVeEB1wp3dsIb8zYyFGOQbCYIgwFD6gDdfS5DOlJEKtIAQRVCxKAhRBCmKfTpZQVEUfCUlWFr7LZ7etAURXsCxx47Oa1ujXX15zUMY4B2CdzY+i+/rvgZBkGgK1YMX4ghGW8ALcYiSCA/n6HI5TeGIaWtx1Jek3KdSFkc8z8OTrAtb8n+uANeGcMRQrPnZECvLfFUZ37ORT+wdVVWhka2y9RVaOEqzOLr5vHMwsLwMBAh8+f1m2Dq433szxpFxPbVW9WoJStmRJEmPBUUQemZDuXAxjvojixYtgs/nw4033gin04k9e/agoaEB48aNA8dxWLt2LWpqaqBpGtatWweO41BbW2sKR9988w3mzp1r7m/x4sVYt24d5s2bh6FDh+Kzzz7DzTffDLfbjSOOOAKALhpdeumlOOKII3DnnXcikUhg+fLlWLRoEZ544gmUlZXh3nvvxR/+8AdceumlmDZtGoCUNWp73HrrrWhqasK1114Lv9+P+vp6bNq0KWMdWZZxww034JxzzkF1dTX+/ve/4/rrr8czzzyD4cOHAwD279+PI488Er/97W9BEAT++9//YuHChVi6dKlZHoObbroJp556Ki644AKzfu6880789re/xRFHHAGe5/Hdd9+ZsVQB4IEHHsDrr7+Oiy66COPGjcP69evxyCOPgOM4nHbaaV24kr2LJRz1IpZoZNGTGCJRQhTMGEeWcNTzSJIEmmJwwyVLAeidSlkWzd+6gnGd2P37IXAcqFgUdFNjxm8WFhY/HcrsdthpGhSZf4wUQzga7B+Fgb4RAIBgvAlAKohxT+L+phaJg8fC8/lnsO3cAXhL4HB0XWDpCguvvRbbtm0zvx922GF5bWdYhs6dNA8DS4bj9GlXIC6E8eC/r8Gn37yFo6f8Em989Aw2bvsKo4ZMBMd1nKktF20sjpIWPvlmIOspTFe1NOEokUjAXqYHE7ezXLvlMt5vHO0wLY4MocjnLgNF0RkDntbkY3GUNcZRgWOBpgtHVX4/Rg8cCAD4aMO3HQqlreNl9SRmnaTVZaHrqpiRJAmE4S5K0ZALGBy7vxEMBrF3714sXLgQRx11FABg+vTp5u9jx45FbW0t5s6di+3btyMajeKUU07B2rVrceaZZ2Lfvn2oq6vDlClTAABr1qzBxx9/jIcffthssw899FA0NjZi5cqVpnC0dOlSjBkzBvfee6/ZZgwfPhznnHMOPv30U8ycOROjRulWW4MGDcKECRPyPqfvvvsOl112GebMmWMu++Uvf5mxjiRJuOiii3DyyScD0N8vp59+Op566incfvvtAIAzzjjDXF9VVUybNg3bt2/Ha6+91kY4Ou2003DeeeeZ35999ll4PB5cddVV5rJ0a6Y9e/bglVdewR/+8AfTsuqQQw5BIpHAY489hlNPPbXoYppZwlEv0luzEBY/TRiGAU1RSAi6qxrHcX0aePOngqIooGkmw8R4x97vzd+6gjHYKnvj72g467epgRgs4cjC4qeGpqqmVUhnEmgYrk+GlREAsHQy9l0PW7eUlZdjc1U1pIpKBGfPASGKqHz5Bbhcro437kGmTZvWpnOeD4Zw5GQ94Bg7KpiBiDJBEASBSSMPx+ABI3DuL68GL8Tx1D/u65YgZggxZowjI7hyEVgcxeNxOGy6S4fTpt8ruQJcG/eXnXVCFPTPkUgEdpsTFEXDZXe3Kxzlg6qqbVIJFtziCJlZaw34dizsTLG3F8NRmO1C2sBRIwqTga4/YFocQQ+QrSoKVFUtuoF3MeL1elFVVYVHH30U4XAY06dPR2Vlpfn75MmT8dZbbwEAamtrMXr0aMycORN33HEHAN3aiGEYjB8/HgDwxRdfwOPxYPr06RmB76dPn477778fiqJAkiSsX78eV155ZcbzP3jwYJSVlWHTpk2YOXNml89p9OjReO6550BRFA455BAMGjQo63qzZs0yP1MUhaOOOgqffPKJuay+vh7Lly/HV199haamJvN5P/jgg9vsq3V5Dz74YITDYdx222044YQTMGnSpIw+/5dffgkAOO644zLqacaMGXj22WdRX1+Pqqqqzp98L2IJR72EninFEo8seha73Y64IEBRVTi6EGvHomOMwPbpEMlZxa52EI1BTGDOCeZAzPlNLfzfrrc6NRYWPzFkRTEDGJMEkfeg2bAIoclU140mmYzfeoqKsjL8EAhCY1nIrB+QZUAQUFJS0qPH6S0oioLD7kBcTAkdMTECaBpoioaNc2BA0j2NF+PdEsRSrmqZMY4KZXFEtBKOnEnxw86179Zo3EMOxg0hTThyOfQA7E6Hp13hKP39mOtdqSgKNCrznad24hnoLQzXRj4tLpUoy+ByuJKbdQ30Wowjs07SLY5IoiAZ6PoD6cIRyFQGQSscQMcQBIGHH34Yy5cvx3333YdEIoHRo0dj4cKFmDJlCqZOnYonn3wS9fX1qK2txeTJkzF58mSEQiHs2LEDtbW1GDt2rCm0hkIhhMPhnMJPU1MTiORz/9BDD+Ghhx5qs059fX23zunOO+/EsmXLsHz5cvz5z3/GkCFDcOmll+LYY48116FpGh6PJ2M7v9+P5uZmALp4e9111yEajeJ3v/sdBg4cCLvdjr/97W9Zy+fzZVquTp8+HbfccgteeuklXH311WBZFscddxwWLlwIr9eLUCgETdPws5/9LOs5WMLRTwiC1GcFVLVvs5BYHNjY7XYkBAGyonQpSLNFxxAEAbVNpzezQ95ZjNlsxek0B2Iay8LhdHanqBYWFv0QRVFAJ8VophOpyFPBj1OWpsbnnrZCKCstBbt3n/mdiukuS36/v0eP05t4PF5EhZD5PSqEoEFDNBHOWC+WiMDtdnf5OIIggGAYc4BfcIujtO+xWAyuZF+BIkk47facwpFRXjvrgpCst0gkAqdNrxunrX2Lo3wmVhRFgUYUR1Y1M4YQUsKRmPYsipIEtqMYRwTZaxPEZiDxDOGIglRgka1YkWVZz6gGQEsKSJIkWcJRngwZMgR33303ZFnG+vXrsWzZMlx77bV48803MXHiRNA0jbVr12LdunW4/vrr4XK5MGLECNTW1qK2thazZ8829+XxeODz+fDggw9mPZbf74csyyAIAhdeeCGOOeaYNut4vd5unU9ZWRn+9Kc/QVVVfP/993jiiSdwww034JVXXjGDfMuyjHA4nCEetbS0oLS0FIDuSrZ582b8+c9/zihjrrY92xjhF7/4BX7xi18gGAzio48+woMPPgiapnHjjTfC4/GAIAisWLEia+KJwYMHd6sOegNLOOolKJKEqmlQVNVyJ7LoMRwOB2I8D0VVLeGol6AoCooiY+e+HzCgdCCC0Rbsqd9u/tYVnEmBiORTLxtC4C3hyMLiJ4aiKFA1DUzS6oImyT7PvpUPZWVloCMpgYVKCgb5BCUtFkpKShDlA+Z343OMT52XqiqIJSLdGqTwPA+kdfoNl7WCxThKTlbyPA9RkuB2pPoKLrs9Z4BrYzDk5DxoEBoAJIUje9LiyOZBOJyfcJRLyMyWVa1QwpF5fE1LCUdSunAkd5hFkCB6L6uaLMsgWvc5KLLHrQsPFGRZBpLtarpwZNE5aJrG1KlTcf7552PRokVoamrC4MGDMXr0aLzxxhtoamrC5MmTAQBTpkzBu+++i927d5vxjQDd1eqZZ54BTdNm8OzWMAyDCRMmYMeOHViwYEHO8hjPYFffkyRJYuzYsViwYAE+/vhj7N692xSOAOA///mPGeNIURR8/PHHpsud0YantwP79+/H+vXrOy3qlJSU4OSTT8ann36KnTt3AtDrCdAttIzYUsWOJRz1EvrgU4GiKJbFkUWP4XA48ME366FqKgYfNKzQxTkgYRgGkizi1hW/wx8vWmIGUDV+6wosy4JmGJBCaiBB8gLclnBkYfGTwhics8l+AduJgaDRl9gb3AYH60aIb0aEDwLouqidi8rKSmjRKCBJAMOADoXM5f0Ff6kPLdtbzO9hvgUEQSASS7NCSkSgaVobF4POIAgCNCZl1VCo4NiGWGMExzasgzxp8Zs8DntOq6F04UgMJF3VwhE4bbqVmcvhQUtkV4fHb/05HUVRoLXuE5Mk5AIIR0YZNVU13+2SkhKOJEUBw2Z/56dc1UioWu/EHMpwvTKOS1GWGJIDWZZTGfuS91ihXSD7C1u2bMGSJUtw/PHHo6amBrFYDKtWrUJVVZUpskyZMgXPPvssRowYYVroTJkyBS+99BIoisoIXH3ooYfiqKOOwjXXXINzzz0XI0eORCKRwPbt27Fnzx7ccMMNAIArr7wSl19+OW644QbMmTMHbrcbDQ0N+PLLL3HSSSdh2rRpKC0thdvtxjvvvIPq6mqwLIsRI0a02x+PRqO46qqr8Itf/AJDhgyBLMt4+eWX4Xa7M2ITMQyDJ598EqIoorq6Gq+++ioaGhpwwQUXAACGDh2KiooKLF26FKqqIpFIYOXKlSgvL8+rXlesWIFQKISpU6fC7/dj69at+Pzzz3H22WcD0C2Kfv3rX+OWW27Bueeei3HjxkGWZezatQtff/017rvvvk5cxb7BEo56CZIk8MpHnyDG86Bpq5oteoaamhp8l0wnma6YW/QcxszjDZcsxcCKg3DuL6/Ghq1f4vl/LelWKmqHywWSTxeOEvCWlXa7vBYWFv0HY3Buo/UBDkdSebs0GR3llR/fhKtmP2immE//racYMGAAAIAOhyCXloEKBeHyevtVMP/S0lLs3LTB/B6MN8HpcCIcC5hBcyMx3QqpOy54usVRWj+PIEAwTMFc1QzhyLAscqcJR+48LI5crBd8cpIjEonAXzoEAOCwufFjU26Lo/RBeq4Bu6qqbdPLE0RBhCOjvlQtFUA5vRySIoO2Zw+abohOusVR75gcybIMtBo/aBQNSehbQbK/IEmSGT/LsDgqZDyo/U0/9ptjlpaWwu/3Y9WqVWhsbITT6cTkyZNxyy23mJMShnCUblk0ZcoUEASBUaNGmZb1BnfffTdWrVqFV199FXV1dXC5XBg2bBhOOukkc52JEydixYoVWLFiBW6//XZIkoSKigrMmDEDA5PZDUmSxI033ohly5bhiiuugCiKWL16Naqrq3OejyEuvfLKK6irqwPHcRgzZgyWLFmSEaePpmncfvvtuO+++7Bt2zZUV1fjnnvuMa2kWJbFvffei/vuuw+LFy9GRUUFLr74YqxZswabkmOx9hg7dixefPFFvPfee4jFYqioqMC5556Liy66yFzn2muvxZAhQ7B69Wo8/vjjsNvtGDx4MI4//vgO918IikLR2LFjB+644w58/fXXsNvtOPHEE7Fo0aJOdVDeffddXHHFFRg5ciTefPPNXixtfowdOw5ffPklnA4HhgwZUujiWBwgXLNwIRZceikAWK5qvYRRr5X+GjOA6q79WwCgW5l3XC4XiDThiBEFeLoRV8PCwqL/kUgkAABcsjNuo0hzWUcYfaJzD/uDmWK+MbwXKz6+sccFHSMgJx0MQC4tAx0MoLrIgnR2RFlZGYLxJvN7KNEMr9eLaCyKaCIMj7MEwYgeBNWIadEVBEGAyrSKo1IA4ci0OCIzLY7caX0Fj8OOUDjcdmPoAhhDc2AZu1n2aCwGR00yxpHdjUgO0QnIHKTnEo70GEet4oAQJNQCWhypigqCIMDQNGQlZT0kKyroDiz5CIKEqvWOBZAkSdAIAkx9XWohRfb5fdVf0K3ZMi2OCiEceb1ecJwNK/5+Z58fGwA4ztZp11u/349bb7213XWOPPJIfPHFFxnLSkpK8Pnnn2ddn2EYzJs3D/PmzWt3v2PGjMkZC8lg1qxZGdnPOoJlWSxevDivdcePH49Vq1bl/H3s2LF48sknM5b98pe/zPh+0kknZQhiBkceeSSOPPLIdo9PEATOOOMMnHHGGXmVt9AUXDgKh8O44IILUF1djSVLlqClpQV33303gsEg7r///rz2wfM87r777qLyvf9jnjeshUVnIEmyW+KFRccYsyYJIQa3s8T8TBBEtwZnXo8nw+KITvDdCshqYWHR/zBEInvSksBOU4jlSI/eGqPt99pLzRTzsiJl/NZTlJaWguE40AHdIscWCGDwuLE9eozepqysDHEhiqUfXA+aYvHdvi8wceIk7N23F6FIMzzOEoSiLea6+Qp4reF5Hmpry/ICCkdMctBsCkdpMY7cdgf2NjVn3Z7nedgYGzjKBkHk9eDa0SgcNj3jnMPmQiyWn3CUy51KVdU2rmpagV3VDJGLJDNT3Suqagq8ubalCApaLwpHkGRUPvMUAN2KJjplGnhLOMqKLMtQycJbHFVUVOCJJx5HKBTqeOVewOv1oqKioiDHtjjw6ZJwtGLFCtTX1+NPf/pTm99uv/12VFdX45JLLslrXy+++CLC4TBee+0101SYoigsWrQIl156KYYPH97hPv72t7+huroaAwcOxLffftu5k7GwsLBIwxBzookIjFdvNBGG0+kyY5d1hRKPB1RdKn0nwSfapAG1sLA4sIknRSJH0rXJQdNobMeKIx2jbUpPMW987mkRmiAIDKiqgvSf9+HduAFEYyNqfjanR4/R20yaNAlHH320KWIcPuRwHHHEEVi/fh2CkSYMGjAcgUgTnE4XbDZbl4UjQRCgtI5FQ/e9cGS8mwzhyHBJc9nTg2PbcmZV43keLG0DS9uhaRoSiQRi8RgSQgy8EIcki5AkKed5pYtFuYSj7BZHBFS174Ujo77UdOEoLV6RpuXOimxmsCMpSL0Y40hz2NFwxm/MZfYfNvd4BsUDBUVR9HhV9XVA8poWKsZRRUWFJd70InrW8tzPAUVRXc6CbNE+XRKOXnvtNZx//vlZfzv44IPx9NNP5y0cffTRRzj88MMz/MtPOOEELF68GB9++GGHwtGuXbvw5JNP4sUXX8RTTz2V9zlYWFhYZMMQc6Lx1GxRLB5GSTdTg3o8HjA7k/7nmgYtkbAsjiwsfmIYg3ZnMiaRk2UQi2YfyLfGsIZ8/ZuV2BfUMz1u3Ke7DvSGCD3voouwdu1aAHpH/LjjjuvxY/QmpaWl+OMf/5ixTJZl/OUvf8Hzbz2CQ3ZvxFufvYTKqu4F/OYFoY3FkVpIi6OkiBWPx0FTFHbU6a5OBAg4bbacMY4SiQQ42g6O0S1rA0lrszc/fhYTRhyCzzf8G4AuSGUTVNLPN1cGJD3GUdvg2EoBAj4b1iiGuEAQREa4Ik3Tcg4+0y2OhF4SciRJgsZykCoHmMts27YWxK2vPyDLMshYFJXPPIWWE35hLrM48Ljjjjvwz3/+M+fvjz76KKZNm9buPubPn4/58+f3dNEOeLokHO3btw9Dhw7N+tvgwYOxZ8+evPe1bds2nH766RnLWJbF4MGDsW3btg63v/POO3HKKadkREm3sLCw6CqGb3gkFjSXhWMBlHQj6w6gWwRQvD6jTYgCoKqWcGRh8RPDtAJJWhy5GAbRaFN7m5iQJInDDzsCP/zwA9bWvQsAUAkV48dP6JW25JBDDsEhhxzS4/stJDRN49BDD8O2rVvx3+/egsfrxmGHHdatfQqiCI3ODE6uUlSX00d3FcPKh0sTjiiSxPwHlgDQBaXLTj4pp2WVLhw5wNG6hZIhHP32F1dh8IAROOHwM7Fy9V2IxWJZ77d04SiXaMbzPCAJZtweqXIAIIl9XldASjCSuiAupOJJ0dDU3guO3SYDHUVBliwxJBuKokDyeNE899SCWxxZ9C7z5s1rNybQ4MGD+7A0Py26JBzRNI2WlpasvzU3N3fKPCwcDmedKfN4PB36h77//vuora3FW2+9lffx8qGn09p2F6M8xVauYsSqq/yx6io7drsdDrvDjH0B6MJRaY2/W3VVUlICItlhJxN6rCOfz3fA1b91X+WPVVf5c6DUVTweB0fTplWIi6ERi8dBkmRefadbb7ulw3UOlLrqLW6//baM792tL1EUobUKjq0m06b35TUw3KeMjH2CIMDtdOKRi3UPAQIEtuzdZwolrcuWSCTAUXZwtB4vy4iRNKRqJGycA9XlQwHoVnPpmYkM0sUfURSznnt9fT1s+/bB9sxT0CgKexdeB7a+HiGi7+9XQ2iTZRkURZn1Z2A8j9nKZf5G0lAVNes63b2vcsWDUhT5gHu2e6LNUlQVYFlIlQNAN6fieB1odWUBVFdXt5tVzaL36JJwNH78eLz88sttoooDwMsvv4zx48d3u2DtmYgC+gvxrrvuwpVXXtmtNKqtIUkSvm5aFvQWVjyU/LHqKn+sumpLWVl5K+GoBaPKJnWrrgYMGACN5wFVBZkUkGpqaoq2veku1n2VP1Zd5U9/rytJkuDiUiKDi2UhKwpsNluPB7ju73XV13S1vhRVhUa1clVLxtjoy/bdSN5gTwpHmqbByXEYnUxrDQA/1jcA0LN8tk72IEkSONoBG6NbHBlCkI3V70sbp/+Px+NZ6yp9gJ7r3CsHDMA2ikZ4VsrtUayqhjse6/N3oTHCUBQFPp+vzbiDIAgwDJO1XM1JYYKmWKiC2m7Zu3pfURTV1uKIJKGp7R+vP9OdNosgCDOrmlFvDofjgK0rC4tC0CXh6OKLL8bvf/97nHfeeTj77LNRWVmJ+vp6vPDCC1izZg1WrFiR9748Hg/CWVKDRiKRduMbrVq1CiRJ4sQTTzS3lyQJqqoiHA7DZrOBZdmc2+fC2L6YoCjKrCfL7LJ9rLrKH6uuclPiK0EompqxCkSaUVpa2q26MjrVJM+D5FOuAoY7wIGCdV/lj1VX+XOg1FVjYyPcTMqtyc3qn3fv3t1jwVQPlLrqK7pbX7wgQHNnDng1Ss+W15ftu3EsI2NfLBYDRWUKD0Z6+cbGRrhcrozfwqEI3MxA0+KoLhkbyRCM7Kzd3G+2uqqv15M/ULS+/2znrmoaNLsjI26ParMB8WifvwuNeGOCICAQCEDVNFBpQg1JEEgkElnLFQwGAQA0yUCW5azrdPu+4vk2gcQ1goSiKFa/IQsJngeM60fq9RYMBrtcVx6Px7JWsrBoRZeEo6OPPhq33XYb7r33XvzP//xPMqCcBrfbjdtvvx1HHXVU3vsaPnx4m1hGoihi165dbWIfpbN9+3b8+OOPOPzww9v8NmPGDNxyyy04++yz8z+pNIq1o6UoStGWrdiw6ip/rLpqi9/vw/4detwRWZERiQVRXl7erboyOukknzCFI6fTecDWvXVf5Y9VV/nT3+sqHA7DyaS6Xq6kiBQMBlFaWtqjx+rvddXXdLW+ZEkCyFZZ1UgKoiT1af3zvO4CbdxTsiyDbBWI2rCokWW5TdnisTjKbSmLI2MSlUsKRsb/RCKRta6i0ShohgDD6ZO/2c5dVRQgS1Y1RVH7/F4VkvUlyfp1UhUFJJkqG0kQOe8JwxqLoTgoStu6TKer91X2DHS6JdmB+lx3p82SZQWacf2S973Ux8+ghcWBTpeEIwA444wzcOKJJ6K2thYtLS3w+/2YMmVKp02tjz76aCxbtgyBQMA0J3z33XchiiKOOeaYnNvNnz8fv/rVrzKWrVixAjt27MDdd9+dM3i3hYWFRUeUlpZi04YtAIBQtAWapqG8vLxb+zSCiZKJBMgED4qmwXFct8tqYWHRf4hEwnClZeByJS2OcmW6sih+FEXJ6lLU50JIMiC1M3lPKYoCSZaxec8eDCovR3M4jH3N+oRItlAQ8UQcnNsOlk5ZFgEpSyOWsYEgCMTj8azHj0QiYDiAZlVz29aompZVONJ6KaV9e4iCAIogIIgSNE2DrCgZFkcURULOcQ2Na8tSXM51egcrxXguFEUBkkHqDQHJEo0sLHqWLgtHgO47OnPmzG4V4KyzzsKzzz6Lyy67DJdddhmam5txzz33YO7cuRmuaosXL8Zrr72G7777DoBuqdTalW316tWor6/HoYce2q0yWVhY/LQpLS1FZegbPQABAABJREFUINwETdMQjOgd7bKysm7tM2VxpLuq2Z3OTiUSsLCw6P9EwxHUZLE4soSj/ossyyAT8cxMYbIEXu3b7FeGFYyHTblCBqNRzH9gCR654lKsevc9fLX5BwDZAwYbWdVIggTH2BGNRkEQJGhaD/tAkiRYhsuZlS0ajYLmVFCMbAbWbo2qqlmsaAhdUOpjBFGEh2UREATznKg0yzGapCDmEB7MDHaMHYrSi9e5Tb20H//1p4yiKgCZDFGStDgyst9ZWFj0DF0Sjr766qsO15kxY0Ze+/J4PFi1ahXuuOMOXHnllbDZbDjppJOwaNGijPVUte/NWC0sLH6a+P1+iJKABB9FMKLHOupu/BFTOBJ4kALfK+mzLSwsiptYLAanPTWwdzCpeDQW/RM+kYBrw3q4NqxPZQprbEAgLZZVn5SD50EShClGAgDLMlj5+6swqLwc15x2Ct5ZU4tV7/67TQxQTdOQ4OOwMclA2KwDiUQCNs6eIVTYWHu7Fkc0K4NkJYTD2bMi57I4aiuQ9D6CIMDDMa2Eo0yLo1zjDjmZmY6jbbrFWQcJfboCSZIgZAlMfR1knx9ULAoqR71aZGahM/5bwpGFRc/SJeHovPPO67CB3LRpU977O+igg/D444+3u84999yDe+65p8N1LCwsLLqLEWskEGlGMNIEmqLh9XoRCnW908ZxHCiGAcHzIHkBLqer440sLCwOKOLxOBzuVCwjmiTB0VTOwbhF8cPZbGgeMQqxyVPMZWJ5JUqIvhVDeJ4HR9OgSBI0SYIgCIiSbGZVc5SXw+t0gGWYNhZHRiwYW9JNjaN1gYhjMjOvcaw9p8VRKBQExUmgWRXBUI6AxJpmWoNkLu574YgXeNQ47PgRUVO4pdOCiVMkCVnMbk1kWhwl60uSpC4l5GkPmqZBRSKofOYpNJz1W3g+/wy2nTtAWi7uWVEUJXVvFdhVraGhoVv9xe7g9Xp7LNFCMRCJRPDiiy9i9uzZGDZsWKGLkxf33XcfPv30U7z22muFLkqP0yXh6Omnn26zLBAI4L333sPatWtx0003dbtgFhYWFoXC7/cDAF774Ans2LcZPp8PZOsYFl3A7nCAFASQAg9vSUm392dhYdG/iCcSiEkytgZCIAhgeIkXHEXnHIxbFD8URUF1OjMyhWksAxp9LxzZkxZsdoYBSZKIJRIZ1jDRRAJOp7PNtsb9xyUtjjjaDkEQwHH2jPU41mYG4W5NMBQAY1NAsyrC9dmzE2uaBrR6lWrJBDt9Dc8LKPGXAEhZ/KW7qlEkBUUVsm6bclVzmN97QziC3YH6U06D7PMjOHsOnGvXoPyHzT16nAMFPdZYZnDsQghHDQ0NuPjiSyAI2Z+T3objbHjiiccPGPEoEongsccew7Bhw/qNcHQg0yXh6JBDDsm6/IQTTsBNN92Ejz/+GEcffXS3CmZhYWFRKMrKylBZUYk1mz4CAMw6ZlaP7NfpdILkedCiAJerbefdwsLiwIbnefxj2078Y9tO0CSJ1af+HDRFmoGNLfofRBZXK0LTQFHdn2zoDPF4HLZk4HUbQ4MgCMiKggdeXQ0Hp1sOvfzhR6iprm6zrSkcmRZHDsSFRlMYMeCY9iyOQnANUkGzKhpCuYQj5AiO3bfCkSiKkBUFPptuvWNY/GW4qpEEVCW7q5MRT8rO6O9xQ0jqSRiGAQGYgqTM+qHZ7D0uUB0oqKpqCkaFdFULhUIQBB5X//4mDKwe2qfH3rNvJx7+220IhUIHjHDUk+hiuGWx1x26FRw7G3PmzMH//u//4oYbbujpXVtYWFj0CSzL4qlVT5nfswUS7QpOpxOkKIASxKyzvhYWFgcuiqJAA3D2wSNwSFWlOX520LQ5ELXof5AkBaL1AFXVQDF9KxzxPA87rb+r7DQFh8OBoUOG4MvtO8008xUVFTjs8MPbbJuyOEoKR5QdQVEEy7mwc58eUJsgiJzBsTVNQyQchc+mgOY0CLwIURSziBwaiiEzmHEOpa2EI6OeAIAkyJzCgyEUOVg9VmFvPL8cx4GUM13lCFkGy1oD32woigIYwh9ZOIsjg4HVQzFs6OiCHb+zbN++HUuWLMHGjRshCAIqKytx8sknY/z48ViwYAH+/ve/o6amBgBw3XXX4aOPPsKzzz6LkSNHAgBuvvlmNDc345FHHgGgPxOPPfYY3n77bTQ3N6OmpgYXX3wxTjjhhIzjbtiwAcuWLcPGjRtBURRmzpyJhQsXwu/3Y9++fWYG9cWLF5vbrF69GtVZBPB0Vq5cieeeew5Lly7FAw88gB9++AG/+93vcN5552Hp0qX49NNPsW/fPrhcLkyePBnXXHNNRhKcSy+9FHa7HSeeeCKWL1+OxsZGjB07FosXL8bApPsvADQ2NuLee+/Fl19+CY/Hg9/85jdZy7Nt2zYsWbIE69atA0EQmDZtGq6++moMGjTIXOfQQw/F5ZdfjkgkgjfeeAOyLOPkk0/GVVddhTVr1uDhhx/Gnj17MHbsWNx8882orKzs8Lr2ND0uHIXDYasDZGFhYZEFt9MJIp4AKQpwOBwdb2BhYXHAYATUrXI5McLnNZczFGX+ZtH/IAiAjIZBiCKoWBSEKAKKDE3r8S52u8TjcTgo/ZgOmgZJkli2fHne2wKAjU65qolRCTElgltX/A4AQFMMxg+fkdVVLZFIQJJksHYVFKuLLaFQCOXl5Rnr5bIr6muLI1M4suuWWKZwlGYNRbRjCSWKIiiSBkNx5veehuM4EFLmfglJgs1my7HFTxvdVS0pHBGFjXHUH1m0aBF8Ph9uvPFGOJ1O7NmzBw0NDRg3bhw4jsPatWtRU1MDTdOwbt06cByH2tpaUzj65ptvMHfuXHN/ixcvxrp16zBv3jwMHToUn332GW6++Wa43W4cccQRAHTR6NJLL8URRxyBO++8E4lEAsuXL8eiRYvwxBNPoKysDPfeey/+8Ic/4NJLL8W0adMA5J/lWJIk3HTTTTj77LNx2WWXmUlpWlpacMEFF6C8vByBQADPP/88FixYgBdffFF3EU3yww8/IBgM4vLLL4eiKHjwwQdx8803Z8Rlvu6669DQ0IA//OEPcLlcWLVqFRoaGjL2U19fj9///veoqqrCTTfdBFVVsXLlSvz+97/Hc889B5/PZ677yiuvYPr06bj11lvx7bffYuXKlVBVFWvWrMGFF14IhmHwl7/8BXfccQf++te/dvYyd5suvdX27dvXZpkoiti8eTP+8pe/YNKkSd0umIWFhcWBhtvlguObbwDAsjiysPiJYQxiqFauOhRBWAOcfoyQSMBZV4fYpKlmAOPEQcMQCWd31+otYrEYHLQ+cHZQVKcy9RliEJsWHFuWZQwqH4jLzrgVgC6k/L9PngfPR9psHwwGAUCPccSp5rLWwlGxYAhFrYUjIkM4yi1oCYIAhmZN4ag3XE3tdjvQSpAiRBFOhz3HFj9tVFVNWRwRBEDmzopnkUkwGMTevXuxcOFCHHXUUQCA6dOnm7+PHTsWtbW1mDt3LrZv345oNIpTTjkFa9euxZlnnol9+/ahrq4OU6boCQLWrFmDjz/+GA8//DAOO+wwALo1TWNjI1auXGkKR0uXLsWYMWNw7733ms/e8OHDcc455+DTTz/FzJkzMWrUKADAoEGDMGHChE6dlyzLuOyyyzB79uyM5X/605/Mz4qiYMKECZg7dy7WrFljlhcAotEonnnmGVPYiUajuOuuu1BfX4/Kykr897//xaZNm7B06VKzvqZMmYKTTz4ZJWlxTF944QVIkoQlS5aY+xo3bhx+/etf4//+7/8wf/58c93y8nLcfPPNAIDDDjsMH3/8MV566SW88MILOOiggwDoVk73338/IpFIn2do7pJwdNxxx2XNqqZpGg466CArOLaFhYVFFn7zm9/goIMOAkEQbV5kFhYWPw16OGu3RYHxlZaivrwcUkUlgrPngBBF+D78AFXVVX1ajng0igqGAaBbHEW7IBxxtC6kcIwdqqLCaXNhaPUocz2GZhEW2lrXGBmkGLse4whIiUnFiCGqeVgWLEWZFkh7GpsQFwQ0h8MIRmPtCkcsbQNLd004UhQFzz77LOLxOE4//fSs8WicTic0QUhmotMbDVIS4fT07UCxv5BhcQRYwlEn8Hq9qKqqwqOPPopwOIzp06dnuEFNnjwZb731FgCgtrYWo0ePxsyZM3HHHXcA0K2NGIbB+PHjAQBffPEFPB4Ppk+fnmFNO336dNx///1QFAWSJGH9+vW48sorM67T4MGDUVZWhk2bNmHmzJndPjdDpErns88+wxNPPIHt27dnCOy7d+/OEI5GjhyZYQ1kCDcNDQ2orKzExo0b4XK5MkQ247y3bt1qLvvmm28wffr0jH1VVVVh4sSJ+CY5mWzQOo704MGD0dzcbB4bgOne1tDQ0D+Eo7vuuquNcMRxHGpqajBhwoQeyT5kYWFhcaAxYsQIjBgxotDFsLCwKABGv2l/NI6ELCPAC4hLMhRVzToZZ9E/cDmd0CQFGstCZvWMnKSi9HmHPhaLwZl0j3AyNOqj0by3TQXH1oUjlrJB1VTQdGaMIppmIUVzC0dsMqsakF04IkAgm8NaX9//xmDRyTBwsIx5/n99/R8YPWggVr37Hr7a/ANGDB+edXtdOOLAdtHiqL6+Hi+++CIAfRB40kkntVnH4XAAmgZCkqAlY0XRggCXc0CbdS0ARU6LcQSAsISjvCEIAg8//DCWL1+O++67D4lEAqNHj8bChQsxZcoUTJ06FU8++STq6+tRW1uLyZMnY/LkyQiFQtixYwdqa2sxduxYM/B0KBRCOBzOKfw0NTWBSFraPvTQQ3jooYfarFNfX9/t87LZbLrlXhrfffcdFi1ahKOPPhrnn38+fD4fCILAJZdc0uY5bt2GM0lh3nBNbWpqyrAsMjAyMxtEIhHTcqr1ert27cpY5nK52hyzo3L0JV0Sjk477bSeLoeFhYWFhYWFxQGLEWT/me9+wLgyP176fitqG5owxOvpsQD8Fn2Pw2YDkQhmLKMkqc+z98RiMTjL9BltB0MjFumcxREBAvuCO1HpGQRREaAqakZ6egCgKTprPC7T4simgiABmiXMZekQBNpkoIOmFVA4ouFkUsLRZXNPxIiaalxz2ilY9o9/okHMni2N53mwlA1MUmjLFvepPdKzsOXKyGYMIEmeh5IUjihBaDOwtNBRFDnT4oiiLOGoEwwZMgR33303ZFnG+vXrsWzZMlx77bV48803MXHiRNA0jbVr12LdunW4/vrr4XK5MGLECNTW1qK2tjbDit7j8cDn8+HBBx/Meiy/3w9ZlkEQBC688EIcc8wxbdbxer1Ztuwc2dqV//znP3C5XLjrrrtMQ5f9+/d3af9lZWVZBfKWlpaM7x6Pp80yYz2Px9OlYxeKvo3cZ2FhYWFhYWHxE8SYJTxz9HAMK/FgweRxiEsy/rxmnfmbRf/DbreDamjMWEbIfR/EOBaPw8HoMYWcDINYrCnvbQVBAElS+Mu7l+Oq2Q/iu31fQFHlNsIRRWYP5B4KhcDaCCMbOlibll04Isk2BkdEAYSjaDQKmiTBUhRcDG3GOKr0+eDgODjKy+G02UGr2V3VeJ4HS9vAUX0hHCWgJAeXJM/3uSVbd9mwYQNqa2vN74cccggOPvjgHj9ORowjwHJV6yI0TWPq1Kk4//zzsWjRIjQ1NWHw4MEYPXo03njjDTQ1NWHy5MkA9Hg+7777Lnbv3m3GNwKAGTNm4JlnngFN02bw7NYwDIMJEyZgx44dWLBgQc7y9LR1jSAIoGk6o815++23u7SvsWPHIhqNYs2aNaa7Wjgcxpo1azIskSZNmoTVq1cjFAqZglh9fT3Wr1+PCy+8sMvnUgi6LBzt3LkTL730ErZt29amwSQIAqtWrep24SwsLCwsLCwsDgQIggDHMvBwLOw0DbtL74KJqmplSurH2O12UK2DGAt9mzlTURTEEwk4Gf2ecjE0YvE4tDxFGZ7nYedcWHDUXahwD8QhB83B/9uwCgSRGXqCIMg2BkNAUjiyp35gbArCWYKDkwQBtBZjNE0XlPqQWCwGF6db8TjplHAkKSlRTFZkMHR2QZfneXCUDSxtAwGiW8JRrgGxIRCRafsm+ES/E46eevIpfLfpO/jdlWiJ1OOHH7bgjjtu7/HjKLJsxTjqIlu2bMGSJUtw/PHHo6amBrFYDKtWrUJVVRVqamoA6CLRs88+ixEjRphWMlOmTMFLL70EiqIyAlcfeuihOOqoo3DNNdfg3HPPxciRI5FIJLB9+3bs2bMHN9xwAwDgyiuvxOWXX44bbrgBc+bMgdvtRkNDA7788kucdNJJmDZtGkpLS+F2u/HOO++guroaLMtixIgRXZ5sOeSQQ/Diiy/i/vvvx6xZs7Bhwwb861//6tK+Dj/8cIwePRo33XQTrrjiCrhcLjz11FNtrALPPvtsvPnmm7jyyitx0UUXQVVVrFixAh6PB7/+9a+7dOxC0SXh6IcffsBvfvMbVFRUYNeuXRg9ejQCgQDq6+tRVVVlBm2ysLCwsLCwsLDQsdvs4OXMwUzCSrHdr3E4HG2EI62PhSPD1cppBMdmGMiKAkEQ8rq3RFGEjbFjkF+3DvA7K5OCU6bIkytYdCgUAs2lRBeKE7MKRxRFgVBaWSxpWp/HRo1Go3Al3b9cDI1gUjgS0wUdSQZrz34N+QQPlnKAIAiwDNdp4ShdLMolHBmDczJ5baEo0ASh37m21NfV42djf4sTJ16Iv699FNv2r+nxY2iaBlVRgHSXX4rKac3VF+zZt7PfHLO0tBR+vx+rVq1CY2MjnE4nJk+ejFtuucV0ozaEo3TLoilTpoAgCIwaNapNpuC7774bq1atwquvvoq6ujq4XC4MGzYsI57XxIkTsWLFCqxYsQK33347JElCRUUFZsyYgYEDBwIASJLEjTfeiGXLluGKK66AKIpYvXo1qquru3SuM2fOxBVXXIGXX37ZdMP7y1/+gjPOOKPT+yIIAvfddx/uvfde3HPPPXC73TjzzDPR0NCATz/91FyvsrISy5cvx5IlS3DLLbeAJElMmzYNV111VUbA7P5Al4SjBx54AEceeSQefPBBjB8/HnfeeSfGjRuH//znP1i8eDGuueaaHi6mhYWFhYWFhUX/xul0Ipo2mFE0DXFRatPptug/OJ1OEGJaUFVFAWS5T4Wj9Jg96f/j8XhewpEgCGCoVCBslrIB0KC2EopUTQVJtRV5mpqaoEGGIhEQ4hQADcFQ25geFEUBaiuhRFVBFUA4SgUSZ7AnEgHDMBCklKjFSxJYL5t1+0QiARutCzgcbTeFu3wxxCKa1XIKR06nEwRJgkzoopYhIPUn4UiWZbQEW+AboWeN8zkq0LCzIW9LuHwxLIvSLY60HG6VvY3X6wXH2fDw327r82MDAMfZOh0fyO/349Zbb213nSOPPBJffPFFxrKSkhJ8/vnnWddnGAbz5s3DvHnz2t3vmDFjcsZCMpg1axZmzZrV7jqtmT9/fkaa+3TOO+88nHfeeRnLWp/bsmXLspa19XqVlZV44IEH2qx73XXXZXwfMWIElixZ0m6ZW+8bQNZM9dOmTcu6bl/QJeHou+++w80332zOEKiqnkFh1qxZuPjii/HAAw/g2Wef7blSWlhYWFhYWFj0c1wuF6JiapAZT4pI/c39xCKFy+UC0ixODNeivhQDo8kMaobFkfE/Go22yfCTDUEQwJCpYN4MxUIDoCiZFhuKIoOm2w4d9uzZjVCzDdFmFj/WehDYa4etOovFEUmC0NSMZYSq9nlw+EgkAhetH9PFMIi2hGDjOCTSRBxeFOHJIbrF4wl4GT1bE8d0XThibLmFI5Ik4XS7EUzum0oKSP1JOGpuboamafA59NhbPmcFRFFAJBLp0fMwBCIt7T7SKLIgwlFFRQWeeOLxrDG++gKv14uKioqCHNviwKdLwlE4HIbX6wVJkqBpOsMcdfz48Vi6dGmPFdDCwsLCwsLC4kDAU1KC8O7UgCIk6IPG/jQYtMjE6XRCkyRAlgGaBplM6dyX2a8Mi6PmRAIJ2WVatRnLO6K1xRFDcdA0DZKcKWpIspA1WxzNUCgfHoWrVMSIIwLYvUFGdH/b82doGhBFMPV1kH1+ULEoyFi0z4WjaCSCMlYX11wso2ekc7mQSEvHHRfEnFZjiUQCXIn+m41xmDGS8sVI+81wapsU4Ol4PB7sT+6bTP7viWxTfUVjox403ufQhYySpIDU2NjYo22e6ZKWFsxdIwvnqlZRUWGJN72Iqqqm0Uo2KIrq84D7PxW6ZBtaWVlppp8bMmQIvvrqK/O3zZs3WybXFhYWFhYWFhatKCkpQSjNHcYQjtIzsFj0L9LTpuv/dQuRvrQiM4SLu76oxfZgGKt/2J6xvCNEUcywOKIpFpqmIhIPYee+H8ALcdQ170EkFsoqpvA8D4dXBsVocHhlOEskJOJt4/7QNA0mGETlM0+BaahHyXvvwrF1C+gCCEfp1lmxRAJOpwP/+nIN3vrqa7z11dfYtm9fTuEoHo/Dxui/cVTnLY6MmEgUK7crHPlKSkxLI8NlrT8JR01Nema/EkeZ/t+u/29ubu7R42S3OCqMq5pF73PHHXdg5syZOf/Wrl1b6CIesHTJ4mjq1KlYu3Ytjj/+eMydOxd//etf0djYCIZhsHr1apx88sk9XU4LCwsLCwsLi36Nz+fDurSBYjD5ub8FyLRIkcp+lYDqcpkCUl9aHBkC0Z+PORxDvW5cMmEM/uc/n3VCOJJAZ1gc6Z/rmnfj1hW/wx8vWoI3PnoGDS17MGHQmLbHjyVQxqYsAGhWgyTJkCQpI/sRTdPQXE7Un3wqZJ8fwdlz4H3vHbBs9lhCvUU0GoWrXHfhc7H6UKi6ZiA+3fkj7nrhJXO9wYMHZ90+wSfA0bqrGkt3zeKIZghQjIpEIve2/pIS0Lt2AwCoeBwUw/Rp7KzuEggEwNIcOFovs9umt3MtLW3jX3WHrMIRWRhXNYveZ968ee0Gs8713Fp0ny4JR5deeikaGhoA6MGnmpqa8I9//AMA8Itf/AJ/+MMfeq6EFhYWFhYWFhYHAH6/H4EEbwaHbeEF0DTdpyKDRc/SOm26YXHUl+6H8XgcFEniYH8JCILAMJ/XXJ4PkiSBIe3md0M4YmkWN/9uBQaUDsS5v7waj75ySxtLKlmWIUkyaDYVSJtOikixWCzDmo5lWZAgIFUO0Ldl/QDDgutj4SgWi8FVUwlAj3EEABdddBH+93//11yHIIisLnSKokAUBdPiyEY7EI93zoKG53lQDEDRGhJ87mtUUlICZvNmALqrmtvj6VcuOMFgEG67zywzRdJw2jym10pPYbqkpV0vtcBZ1Sx6j+rq6i5nVbPoHl0SjgYPHmyqeRRF4cYbb8SNN97YowWzsLCwsLCwsDiQ8Pv9kBQF7+/aCzfL4P0f98BfUtKvBoMWmbROm04mEqBZNmssoN4iHo/DztCpATpBgKPp/C2OBBHONIsjmtTFlLgQw9DqUQCAAZwDvBhr41ZpuGlRTMriyPjcOk09wzAgWlmBELLcp8KRoihICAJcycxzrmSso1gsljXwd2uMOjWFI8aBcHxPp8qQSCRAMxooRm33Gnm9XhANDbBt2wr75k3w9DPLxHA4DBeX6Vrn4jwZsXF7AkMg0tKun2IJRxYWPU6XhCMLCwsLCwsLC4vOYUy6PfT1enPZITNmFKo4Fj1AuqsaoAtHrj7OkpdIJGBPcwkDADtD5x17R5IkUywCAJpKZmWLhaCqKkiShKZpCEcDbbK0mfF6mJTFkfG59fE5jgOpZApHlCL3qciWKwNdvoHEjXMKJ1ogSAmomoJIJNKpMiQSCVCMCorRkGjJfY1qamoAAGWr/w8AMGTChE4dp9BEIhHY6UxrSgfrMa9BT2EKR2RmjCMhR8Y6CwuLrmEJRxYWFhYWFhYWfcCQIUPwyiuvZKTgtjKq9W8oioLN4UhZHPE8XK6+F45srdyq7HT+wpEoiqC5NOGI1C2AVE1FJB6E1+UHL8QhiDxKS0sztjWEI5JOE46Sn1tbHHEcB0iZwhEp9a1wZAhETiOrWlI4ylfMMCyEXl27FDW+EdjasA4RKdipMsTjcZC0koxxlPsazZo1C5MnTzYzSPWnwNgAEAyGoGoMBCmBEN8MQUqAo+wIBAI9ehxTOEp/BigaomVxZGHRo1jCkYWFhYWFhYVFH2HFMzrwcLk9oExXtTh83r4VAwVBgI3OFI44mmo3Y1c6siyDsqeGBIbFEQAEI83wuvwIRPQMWeXl5W2ODQAUnXJVM0Sk1sfnOA6ElGkFQsqFsjjSz9ee/N9Zi6MLDr8BA0uGY8qgWfhw66udKkM8HgPJyKBZNWv2uXT6c8bFuv370dzSjD3BbXhn47P4vu5rTBp4JPbv39GjxzFd0uhMiyNRtIQjC4uexBKOLCwsLCwsLCwsLLqIx+M2XdVonkdJRXkHW/QsPM+DI8mMZRxJtbH4yUVrVzWKTA0Pbl+5AEOqR2JPvT7Yr6ysbLMtAKQfnqS0jN8M7HY7tFbuQ4Qkwm63o68wLIYMFzWKIGBnmLzjQRnrDS0bC46xo9RVBVESoShK1mDa2YjGoqBZ3VVNlhWIotjnmeX6AofdiUEDx2JgyXCcPu0KCFIC/9n8Klxcz1rkpSyOUvetRlMQE/mJgRYWFvlhCUcWFhYWFhYWFhYWXcTn9YIMhgDowlHrzGO9jSAIbYUjiuyUxVG6cEQSJAgQmD5juumaNmr8UPh8PlRXV2dkxTIG7QSVclUjyOzCkc1mA2QZUFVTaSLEvhWODMuiulgc3v/P3n2HN1n1bwC/s0fTdLeUskH23oiIAiIIKoKvP1BEGUUoS9Yr+CqKiCAgIEVAGQo4QEXgFREHCvgqoDJkiMi0UEZLd5udPL8/Qh6aJm3T0jZpuT/XxUXz5BnnOYSmufs956iUyDCZoZbLfa44ujU5tubm385Jsk0mE4KCgnw6R25uDmQqh7j6nMFgqJLBkQABIdooqBQaRCtqAHBWswmCUMyRJeNtqJrAybGJyhyDIyIiIiKiUtIHB0N+PQUAIDMZK3zeKovFAqXMPThSSiRuc2kVxWazuVUZAYBCrkTbtm0xYMAAcZtMJvNYAdB2c5U0qfRWGOD62m63u+2r1TpDFonFAkGtBgAIFRwcuYKfl//3K+bf2xmb/zoLu8Puc8WRa6iaSq69+bdGPK+vwZHBYIAqWIAsX3BUmYekFcbhcEAqcX9dSiUyOBwOpKamisFmbGzsbV3H9ToX5O5zHPkrOEpJSUFWVpZfrh0SEoLo6OgSHbN69Wp89NFH2LNnD65cuYLHHnsMb7zxBnr27FlOrfSvHTt2QKFQ4MEHHyy3a1y6dAkfffQRTpw4gfPnz6N27dr45JNPPPYbMGAAunbtiunTp5dbW8oSgyMiIiIiolLS6/WQuyY5NhorvOLIkJsLh82OsxlZkEiA+qEhsAsCLD5W0XgLjmRSuRgKFcUVDuXPB1xfFxYcRW/+GHadDhKHA7DZKjQ4MhqNkEkkWHTf3YgLDsKY1s3w5m9HfZ5I3Gg0QiFTQnZzBS/VzYojX4Mn575GaBXuFUdVVcGgEXBWyA0bNkx8fN9992PhwgWlvkZhq6r5IzhKSUnB8BHPwmL2T2ilVCnw/roPShweuURGRmLNmjXiCqBV0VdffQWNRlOuwdH58+fx888/o1mzZhAEQZzgvrJjcEREREREVErBwcGQ3EhFjXWrIVgsFR4cpd64gRtpaTiScgNyqRRbB/RBcm4ugOs+HW+3ew+OCgY/3riGHeWlK2AxOhMjbagzcCr4Yalhw4bo0aOH2xA6eZ3aaNmypU/tLAsmkwlapRINwpwrlGl0cgQrFCUKjtTKW0GX+mbF0bVr1/Dnn3+K2xs2bIhOnTp5b4PRJM5xBPg+MXdlI5FIPIalCYIAm935+kj416vY+uM6pKSk3NZ1xIBTnm+OI5nMp+CzrGVlZcFitqLxfTegDa3Y8MiQqcBfeyKRlZVV6uBIqVSiRYsWZdyyO0+3bt3QvXt3AMBrr72GU6dO+blFZYPBERERERFRKd1///0wGAwQBAFyuRwdOnSo0OsH63S4SynHE40bwFXgUT80BDe0vgVYNrsd2aY0XEo/A4lEghphDSCRSEv0wfv4N84PqhKpgK7DLntvZ3Cw34dkmEwmqBXuH3/UMqnPE4kbjUYo5beCI9dQtV27duHAgQPi9lo1a2PLF597HG+xWGCz2SFTCpArHOI5qyKZTAaH4B4+OgQHBIczTGrd6G6cSTqO01d+v63rWK1W55xZ+aqbBJkcNj/OcaQNtSI4svLNseRtqJrD4cCGDRuwbds23LhxAzExMXj88ccxZMgQ8TjXcLfVq1djwYIFOH36NOLi4jBp0iR07tzZ7Ro7duzAJ598gqSkJISEhKBfv36Ij4+H/Gbwl5OTg8TERPzyyy/IyspCaGgoWrZsiblz5/r0fFHGjh2Lw4cPA4AY7I4aNQrx8fEAgK1bt2LTpk1ITk5GeHg4HnroIYwaNUps244dOzBnzhysWbMGK1euxIkTJxAeHo4RI0bgkUceEa8jLTDnnK+ys7Px/PPPw2q1YtmyZQgLCyvVecoLgyMiIiIiolKqXr26+MHDHyQAwjQqsYoGAHQKBdK9DBPyxm63YfepT7H71KeQSRVY/MROSCUSnyqOXFo8mAKFxn1/b8OU/M1sNkNVYPUzlUyGTB/DG5PJBJVcLT5W3pwkOzs7G03rtcP0YW/hy30bsfv3LV6Pdw1LkysckCkFt21VjVwuh83hHj7aHFZAIkCpUEEhV0Kr1iE3N+e2ruMKjhTXr93aKJNycuwykpiYiE2bNuGZZ55B69at8euvv2Lp0qUwGAwYOXKkuJ/NZsOrr76KJ554AiNHjsT69esxY8YMbN++HSEhzu9NH3/8MZYvX47Bgwdj4sSJuHjxIlatWgWHw4Fx48YBAJYuXYr9+/dj3LhxiI2NxY0bN7B//37xOsU9X5Tp06fj1VdfhUqlwsSJEwFArM769NNP8dZbb2HQoEGYPHky/vrrL6xevRo3btzASy+95Hael19+GQMGDMCwYcPw7bffYu7cuYiKikKXLl1K3c9paWmYOHEiNBoNVqxYUeGVq75gcEREREREVEnZHQ5I5QUnIfYt+HENJ3uw2VC0iLtbDHtkUoVPx7uWoNeGWaHWOfe3WZznKO1v3cuTyWSCquBE4jIZLD6uQGcymdwrjmTOECk3Nw/VgmMAADqNHjm52V5XD3NVF8kUDkhlAiTSqhscSSQSZBpS3SrZrHZnZVeQRnfzbz1yc3Nv6zo2mw0QBMRs/ACAc5ha5n094ShB8EneZWZm4tNPP8WTTz6JMWPGAAA6d+6MvLw8bNy4EUOGDBHnLrNarUhISEDXrl0BAHFxcXj88cfxyy+/oG/fvsjLy8Pq1asxdOhQJCQkAHBW/chkMixbtgxDhw5FSEgI/vzzTzz44IPo16+f2I7evXuLXxf3fFHq1auHoKAgaDQatyF5drsda9euRc+ePfHvf/9bvE8AWLVqFYYPH464uDhx/759++LZZ58V90tOTsbatWtLHRxdu3YN48ePR7Vq1bBw4cIKnfetJALvOzoREREREflEEARI4V7dI/Uyv4w3rnAoUheLmuF3oUZYAwCAXCb3aUJXV3Ak5NtVcEjcngskVqsVSmnB4EhasuBIqhIfK+TOr40GA3RaZ1VFkEYPh8PhNRC5FRwJkEgAhUJSZYeq5eTm4OSVA1j0bQIWf+es7jBYnH2i1TirKYI0wbBYLW7zXpWUzWYDlEpcf/pZXH/6WaQ8+TQEmQyCw+HT/wEq3IkTJ2Cz2dCrVy+37b1794bRaMTp06fFbVKpFB07dhQf16xZEwqFQpzD6vjx4zAYDOjZsydsNpv4p3379jCbzTh37hwAoFGjRtixYwc+/PBDcVt+xT1fGv/88w8yMzO93qcgCPjjjz/ctt93330ej0+dOlWiKk2X5ORkPPfcc6hTpw4WL14csKERwIojIiIiIqJKSxAEFBwVJpFAnEumKK5wSCpxD3kkEqlPwZE4L8kNJZQaE8wGGUw5znMpFApfml+hLBYLFAWCI4VUCqvJ4tPxJpMJCtmtoWpSiRQKmRJGkwlBN8MQnVYPwDlRclBQkNvxrpDIlCtDUJgEUrlQZYOj6tVjESxE47E2Y8VKNgHOMCdI7ewrrdpZeZSTk1PqoNFut0Mil8MaU03cpkhNFZ+Ty/lxt7RycpzDCCMiIty2ux5nZ2eL21Qqlcf/eblcDovF+X8rMzMTANxW1Mvv+nXnZP7Tpk2DXq/Hxx9/jMTERMTExOCZZ57BoEGDfHq+NFz34ct9AvCYeygsLAw2mw2ZmZke5yjOyZMnkZ2djSlTpkCpVJa06RWK/5OIiIiIiO5ArnCo4HxEUonUp9+eq1TOiptTP0Shdf/r+OeIHhnJzt+YB+KHIJvNBsHhwNmMLADOgE0ulcLi43w4ZrMFSnmo2zalQgWTyYQgjTMwcv3tLThyTcLt6i+JtOoGR3q9HoZrOagZfpe4zeawwCFxQKNyBkb5g6PQ0FCPc6Snp+Pdd9+F1WpFu3bt3IYnudjtdufk2PkINx/bbDYGR7dBr3e+ltPT091WaktLS3N7viTnevPNNxETE+PxfPXq1QEAOp0OU6ZMwZQpU3D27Fls3rwZCxYsQL169dCmTZtin7/d+8yvsPvMyMhw64+MjAzI5XKvr+Hi9O7dGzKZDC+//DLeeuutCl9coSQ4VI2IiIiIqBIrWFskCL5NTn0rOHL/SCCBbxVHruCoYbc06CIsaHB3Bpr0SHV7LpBYrVakGU2Y/OPPmPzjz5i2Zz/kEimsVt8qjixmCxQy90BMLlXCZrOKFUeuv13VGvm5QqKWfa9DF2GBQuXweUW3ykatVsNicw/FzDYjHA5BDIzyB0fenDp1Cvv27cP+/fvx5Y7tXvex2+1iUCS6+bg0Q4folmbNmkEul2P37t1u27///ntoNBo0btzY53O1bNkSarUaKSkpaNKkiccf1wTa+TVo0ADPP/88AODixYslft6b/FVQLrVr10ZYWBi+//57t+3ff/89JBIJWrdu7bZ9z549Ho8bN25c6qq5KVOm4KGHHsK0adNw5MiRUp2jIjCCJSIiIiKqpKQSiUdyJMBz+Jo3rjlgJAXmSPJ1RTS12jlsSxVkh0whQBtig83s/NAeiHN12O12VAvSYnpHZ2WCRAL8ejUFvk6FYzabEe4RHDk/ThUcflVweIvreAAIqWaGVOacJLuqBkcajQZmL8ER4PA5OHKtjBbbOAeWbO/hnsPh8Kg4cr34fQk/qXChoaF44okn8NFHH0GhUKBly5b4/fffsXXrVsTHx5fo/7hOp8Po0aOxfPlypKSkoF27dpBIJLhy5Qr27duH+fPnQ61WIz4+Ht27d0f9+vUhlUqxc+dOKBQKMbwp7vni1KlTBzt37sRPP/2EyMhIREZGIioqCiNHjsSiRYsQFhaGe+65B6dPn8bq1avRv39/sRrK5euvv4ZKpULjxo3x7bff4ujRo1i8eLH4vMlkws8//wwAuHr1KvLy8sTwrW3bth5D3QDghRdegMViwZQpU5CYmIjmzZv73LcVhcEREREREVElJZVJYS+QfDgEQFqC3357C4p8mVjYtaKS3XLreLtV4vZcIBEcDqjlcjQIu1Xd8Pu1VJ8nUTYajTAq89xWCnNVa7lCELVSC4lE4nVybJPJBIkEcBV4SeS2Oys4spogxa2+0tz8Oy8vz+s5XEGbXOWAweI9OHL+27m/fgWpJN9zFc+QWfHze5XXNSdMmAC9Xo/t27dj/fr1qFatGiZNmoQhQ4aU+FxPPfUUoqKi8Mknn+DTTz+FXC5HjRo10LVrV3FIYcuWLfH111/jypUrkEgkqF+/PhYtWoS6dev69Hxxnn76aVy+fBmzZ89GTk4ORo0ahfj4ePzrX/+CTCbDpk2bsHXrVoSHh2Po0KEYNWqUxznmzJmDFStWYN26dQgLC8PMmTPF1eQA55C3F1980e0Y1+MVK1agXbt2HueUSCT4z3/+A7PZjOeffx7vvPMOGjVq5FvHVhAGR0RERERElZRMJoNdsLltszscJRw2UaDiCL5VHLkqDmyWWxUfrq8DMjjyNpE4fA8YsrOzcNXwIw4n/QiZVIHFT+yE4+axGrVzPiOpVAqNOqjQ4EiukIhtkMkdMJmq5hxHWq0WJqtBfCwIAkyWPMgFBTQqZ1+pFOpCQzbAGRxJZYBMLsBq8T4PlcPhgNfZ4VHxFUchISFQqhT4a09khV7XRalSeB3yVZT4+HjEx8cDcM4zdPDgQbfnpVIphg8fjuHDh/t0jvwKDukCnHP69O7du9BzTZgwARMmTCj188WJjo7GkiVLvD43cOBADBw4sNhz1KxZEytXriz0eW/96M22bdvcHstkMsydO7fY4/yFwRERERERUSUlk8lhL/Ch2i4IkJVoUmChwCPfghSZTAaNVu0eHJmlkEgCMzgCvA3L8/1+lUoVWla7F10bPCxWabmGqmlVtybC1hYRHMnyFYZI5QKMVTQ40mg0MFuNN8M6CWwOKxyCAxaLBWqV87UhlUqhUWkLrTiyWCyQySWQygSYi6g4EjxyTt+Cz7IWHR2N99d9gKysLL9cPyQkxG3SZqKyxOCIiIiIiKiSUiiVsJrcP3hbHQ7IFcUPXRGXSfdScePrPEc6XRCsplvBkdUshUarKfVEseWtYEgkCL5XWDkcDoRpo91WCpPeXGtInS840qiCvIYhJpMJMvmt68vkAsxVODiyO+ywOaxQyJQwWV39IYgVRwCgLi44KqbiyDv/DFEDnOERwxv/sNvtRVYPcoW928PeIyIiIiKqpAQAGSYzjDYbMkxmGKw2WO12KEoSHHmEKQ6fgyO9PgR55lshkdUkg14f7PsNVCCpVCoOLXMRAEilvt2r3W6HVOr+8cnVd64qGgBQKTVewxCz2QxpvuBIKheq9BxHAGC2GqCQKWG23bpPtfLWpMrFVRxJ5QKkcgEOhwC73e49kPRYVvC2m0+V0KBBg3D16tVCn/dl+FhR+vfvj/79+9/WOSozBkdERERERJVUZmYmklPTcD4zG5v/OosjKTfQKioCwSpVscdKb65GVXAuGIfgEJ8rTlhoODJT81UcmaQIDQn1/QYqkDM4ct9md/h+rw6HAzJpgeBCAKQSGS5fP49qETWQmZsOh8MBg8Hgcbyz4ujWEvEyhQM5RnOJ76MycAVHF9NO4a7o1riaeUF8Ln9wpFRoYDR6r7qyWCyQygRIZc5/NLPZ7DEEUiqVQuJRZXJztUAfw0+qGhYtWiSuxEdlLyCCowsXLuD111/HoUOHoNFo0K9fP0ybNk1c4rMwCxcuxJ49e8RZ1evWrYsRI0agX79+FdRyIiIiIiL/qVGjBrSGPNQL1WNM62YwWG1YffwUVD4ER67qDYdgd9vuEHyfXDs0NBS2S7eWqLca5QivGVGCO6g4MrkcBpsVZzOyEBcchAyTGalGk7gyWnEEQfAY1uaAAKlUitnvjcbM4cvw330bcf7yKUTX8Jyk2GQyQZI/OJIL4sphVY0rOFr90yxM7LkE3/75sficMl9wpFYWHhy5KrRcwZHFYvEIjiQSiXO8Yf5tNx/7GghS1dCgQQN/N6FK83twlJ2djWeeeQbVq1fHsmXLkJ6ejnnz5iEzMxOLFi0q8lij0YjBgwejbt26EAQB33zzDaZMmQKHw4GHH364gu6AiIiIiMg/goODkSWTQiOXQ6Nz/mhvcQg+BUeuOT/sjoKrstl8ng8kLCwMWdfl+Pt/YQCA9MtqhLYILcEdVBy5XI7reUZM/vFnzL+3s1ihFevjnDTOIXzSAtucc/ZMGboA1SJqYOhDk/Dx14kwGjzDEKPRAKlbxZEAs8kiTiBdlbiCoyc7TkWN0Pq4u34/JKWfBuBcTc1FKVcVOlzParVCKnWIw/u8VZPIZDKP4MhVVsbgiKjs+D042rRpE7Kzs7Ft2zaEh4cDcH4DmDZtGsaOHYv69esXeuysWbPcHnfr1g1nz57F1q1bGRwRERERUZWn0WhgsrtXDJnsdp+CI4lEAplMdlvBUbt27XDk6CE4rM7hbvUbSNCxY0cfW1+xFAoFQtVqzOjUFnHBQRjTuhk+/PNvXBR8rzjy3OaAWqVBneoNAQDVVFqEh0Tjeu4Zj30NBgNkivyTYzsgCAIsFotP/16ViWvkSIgmEiqFBhqlTnxOqVDl+1oNk9FzWB/gDIokMgek0lsVRwXJZDLAZoXi+jXYwsIhy8uFLCP91nNEVCb8Hhzt27cPXbp0EUMjAHjwwQfx4osvYu/evUUGR96EhoYWOsEaEREREVFVolKpYLYVCI5stmKnfHCRSWW4kXsFZqsRWaY0mK1GWG0WnybXBoBWrVohcdk7JW63PyiVSkACNAhzDiPT6OQIUsihkpT+I5FDsEMldx8+pZArvS4fbzDmQaa6NZ+UK0QyGo1VLjgSJ8e2OSuvLLZbFVhK+a2hjQqFCrnmDK/nMJvNzuBIXnhwJJfLAbMZMRs/QMrgp6A/8AvUFy/ceo6IyoTf6/fOnTvnEQ4plUrUqlUL586dK/Z4QRBgs9nEqqWff/4ZTz31VHk1l4iIiIgoYGi1Whht7hVDRqvNYy6YwggAfjz9OS5nnsOWQ8ux6NsEWO1mn4OjykSpVMJcYCJwi90BpdK30MbbcDJBEKBUKN22KeRKWL2EHCaTyb3iSOFsS2Fz/FRmruDStZqa2WYUh44p8gdHcqXXQAgALBYzpFIHJEVUHMnlckAqxfWnn4U1OgaZPR9AdqcuAFhxRFSW/B7DZmdnQ6/Xe2zX6/XIysoq9vj9+/dj+PDhAJzfOF5++WX06dPnttoUaN9kXO0JtHYFIvaV79hXvmNf+Y595Tv2le/YV75jX5VMVeivoKAgGK23giNBEGC0WhEUFOTTfemCdGgc3gk1QutjULvxMFkMWPzdeKjVarfjq0JfqdVqmO3uwZHZbodao/HpviQSKQShwAp0sEMuKxAcyRSwWKwe5zSZTAhReFYcWSyWSt2v3rheP66KI7PNCIVCCbPZhCup/6BGTD1k5qbDaMqFxez9/q1WCyT5VlVzODwnbVepVJA4HLDGVAMA2JThcAQFQa5QsOKIqAwF7P8mXyeJa9myJT7//HPk5uZi3759mDNnDmQyGf71r3+V6rpSqRRhYWGlOra8eQvYyDv2le/YV75jX/mOfeU79pXv2Fe+Y1+VTGXur8jISJhtNtgcDsilUpjtdjgEAVFRUT79TBsUFASNUgeVQoNoRQ3Y7FYIEBAeHu71+MrcV2FhYR7D+sx2O3TBwT71lUwm81iBThAEyGXyAvvJYbNZPfrKZDQj3K3iyPm1XC4P2M8ft0Oj1opD1MxWE1Q3g6O568aLK9CdPPcbatao5fV15XA4IM0XHKlUKo9+Cg0NhVCg4g42GxRe9iWi0vN7cKTX65Gdne2xPScnx6f5jXQ6HVq0aAEA6NKlCywWC+bPn4+BAweWKrl3OBxe2+NPMplM7Cd7gckPyR37ynfsK9+xr3zHvvId+8p37Cvfsa9Kpir0l+sXrQarDXqVEnn5qo8yMrzPHZOfUqkUhxMBgMXu/Nput7sdXxX6CgBMNveVuYx2B/RyuU99JZVKPSYSdwgOyGXuw/pkUhnsdrtbXwmCALPZApncfXJsAEhNTfXp+pWNWq3OV3FkgEwuByDBK6PfFVeg2/HThzh37Q+vryujyQipXID05ke6jIwMj36y2+2AwwHY7cDNz34Smw0KhaLUfarX60tdAZaSkuLTqJnyEBISgmgfVwh0Wb16NT766CPs2bMHV65cwWOPPYY33ngDPXv2LKdW3rmGDBmCJk2aeCzwVVn4PTiqX7++x1xGFosFSUlJGDRoUInP16xZM3z44YdIT09HVFRUqdoUqG+Gdrs9YNsWaNhXvmNf+Y595Tv2le/YV75jX/mOfVUylbm/XJMQG2zO4MhwMzhSq9U+3ZNarYYl3xw7Zqvza5VK5fX4ytxXSqUSJqvNbWSD0Wb3ua/kcjnsBYaqCYIDUql7yCCVymC72U+u85rNZgiCIIZFAMRJnw0GQ6Xt06Ko1WqYbr6eTFYjFAo55DK52wp0EfponEl2eH1dWa1WSJQQ5zgym80e+7jm4pLYrBBuhj1SqxVKpffXb3lKSUnBiBEjYDabK/S6LiqVCuvWrStxeOQSGRmJNWvWoFatWmXcMqoK/B4c3XvvvVi5ciUyMjLEcsLvvvsOFosF3bt3L/H5Dh06BJ1Ox9JEIiIiIqrydDrnMue5FisQBORZrW7bi6MN0sCUnS84ulkh4gqkqhKtVguHIDjnNbo5/43Jbvd5InGJRIKMvOtuK9A5HHZIJVKP/SAIbttcYYLZIEPODWfYoQ11hnwmkwmB6vz588jJyYFEIkHDhg19Xq0PAIK0WpisBgDOiiOFUuFRySORSgsNeGw2GyTSW0PVbAWHpAHianQSqw3CzTnOJVYr1OqKX6UuKysLZrMZj42+F1HVQyr02qlXsrD1vX3IysoqdXCkVCrFkTx3Crvd7hxuyvmwiuX3Hho8eDA+/PBDJCQkICEhAWlpaZg/fz4efvhht6FqL774IrZt24Y///wTAPDXX39h0aJF6NOnD+Li4mAwGPDjjz/i888/x9SpU/mPT0RERERVXlBQEIBbgVHuzb9d24uj1Wrxe/I+HDz/DQDgYtopcXtV47ong9UmBkcGq9Xne7VYzDh44Rt0qtcH3578EH9dOwSVXAMUmJdVAgkchQRHF34Lw4XfnFU03YZfAuCsrAlEGRkZGDdunPh42LBhGDJkiM/Ha4K0MKflAQBMVgMUaoXHHLZSiRQOR+HBkUoqwJXLeQuOXAGn1GqBq5ZLYrVCo/Zf8BlVPQSxdSL9dv3S8jZUzeFwYMOGDdi2bRtu3LiBmJgYPP74426vA9dwt9WrV2PBggU4ffo04uLiMGnSJHTu3NntGjt27MAnn3yCpKQkhISEoF+/foiPjxc/u+fk5CAxMRG//PILsrKyEBoaipYtW2Lu3Lk+PV+csWPHQqPRoGfPnvjggw+QnJyMNWvWIDo6GitXrsThw4eRlpaGqKgo9OzZE6NGjYJSeWvy+06dOmH8+PEwGo344osv4HA4cM8992D69OluYfuxY8ewaNEiXLhwATVq1MCECRO8tmfv3r1Ys2YNLl68CJ1Oh/vvvx/jx48XvycdOnQICQkJWLp0KbZt24aDBw8iODgYCQkJ6Nu3LzZv3oyPPvoIBoMB999/P6ZPn+7W3rLk93RFr9dj/fr1eP311zFhwgSo1Wr0798f06ZNc9vP4XC4pdGRkZHQ6/VYsWIFUlNTERwcjHr16uGdd95Br169Kvo2iIiIiIgqnFhxVCA4Cg4O9un4OnXqYN++ffj410XitiBtECIjK98H3+K4PoylmUxQyZ2VL3kW34Oj0JAw1A9tLa5AZ7Ya8fbuyV73LbjEjyscatA1Dfoo57LyEonzT2HL0ftbSkoKAGBGpzZYc/wvXLt2rUTH63RBSL12Mziy50GhUHhUZ0mlMo+QzcVms0EtvTVUzVtlkuvDuiRfH0qsFgQFVb3g0x8SExOxadMmPPPMM2jdujV+/fVXLF26FAaDASNHjhT3s9lsePXVV/HEE09g5MiRWL9+PWbMmIHt27cjJMRZffXxxx9j+fLlGDx4MCZOnIiLFy9i1apVcDgcYkC5dOlS7N+/H+PGjUNsbCxu3LiB/fv3i9cp7nlfnDp1CtevX8dzzz0HnU6HmJgYZGRkQK/X4/nnn0dwcDCSkpKwZs0apKWl4eWXX3Y7/rPPPkPr1q3xyiuv4J9//sHy5csREREh3kNaWhomTZqE+vXrY+7cucjJycGCBQuQl5fndp59+/bhhRdeQM+ePTF27FgkJydjxYoVSEpKwvLly932XbBgAR5++GEMHDgQ27dvx2uvvYZz587h3LlzeOGFF3DlyhUsXboUcXFxePbZZ0vUH77ye3AEAHXr1sXatWuL3Gf+/PmYP3+++DgyMhKLFy8u76YREREREQUsrVYLiUTiHKoG55A1uUwmDuEpzpAhQ/DEE0+4bZNIJJBKpYUcUXm5PsBO+fEXr9uLow3SQmXViivQOQkQCs57BM/VoV3VMrowK4Ijb1UYyeQSr5U0gcA1uXTTiHDUDwlGRnp6iY4PCgrCJVsqAMBkzYNWoYOk4LA+OCcO98Zut0MiFcSCrqKCI/3/9sGhdVbZqa5eQVDLliVqK3nKzMzEp59+iieffBJjxowBAHTu3Bl5eXnYuHEjhgwZIoauVqsVCQkJ6Nq1KwAgLi4Ojz/+OH755Rf07dsXeXl5WL16NYYOHYqEhAQAzuodmUyGZcuWYejQoQgJCcGff/6JBx98EP369RPb0bt3b/Hr4p73RU5ODj744APExMSI2yIiIjBp0iTxccuWLaHRaDB79mxMnz7dbYhmREQEXnvtNQDOxblOnTqFH374QQyONm3aBABYsmSJGOAXPD8ArFmzBk2bNnWrltLr9Zg1axYOHTqEdu3aidt79eqFESNGAHDO6bxnzx58++232LJlizjP16FDh7B79+6qHRwREREREVHJyWQyBGk0tyqOLFYE64I8goviznEnqFmzJl5//XUYDAZxm0wmQ/v27X06Xq1WwWJyn/jYITg8hlo5HA5ICgRvrtCjQG4CiTRwF+ZJT0+HBIBepUSoSoVz6WklOj4oKAhGay4AwGDJRTVleMFRfZBIpBAc3oMjZz/eqszy1k/R0dHo0LEjsrKzAfvNQC4uDnfffXeJ2kqeTpw4AZvN5jGap3fv3ti2bRtOnz6NNm3aAHCuONixY0dxn5o1a0KhUIhVa8ePH4fBYEDPnj3dgtL27dvDbDbj3LlzaNu2LRo1aoQdO3YgIiICXbp08VhlvbjnfdGgQQO30AhwhpebNm3Ctm3bcPXqVbcJzpOTk92u06lTJ7dj69atix9++EF8fOLECbRr186t6rNz585uw4cNBgP+/vtvjyFsPXv2xOzZs3H06FG34KhDhw7i1675nNu0aSOGRgBQq1YtHD582Od+KCkGR0RERERElZhOp0POzYqjHKsVOp1vw9TuNBKJxO3DWEmp1CoYbO4TWTscdtg9giM75DL3j1liVY1HcOIMSAJRRkYGQjRqyCQShKlVyLh+o0TH63Q6GMy5EAQBBnPOzSq4AvNBSSQQUFjFkQMSifO5wgI2pVKJ12bPLlG7yDc5OTkAnNUy+bkeZ2dni9tUKpVbiAE4VyF0DcPMzMwE4Jwny5vr168DAKZNmwa9Xo+PP/4YiYmJiImJwTPPPCOutl7c877wtojWpk2bxMqndu3aQa/X488//8TChQs9hpIWXHgg/30CzqFqNWvWLPK6OTk5EATBo2/lcjlCQkLc+hbwHHqsUCg82qFQKMp12CuDIyIiIiKiSkwXrMOWv8/DLgj479mLaNK4sb+bVCVpNBpk2DPFxw6HHQIE2B3uQ81sDlvhC/UUzEgElKg6rCJlZGQg7OYQnTC1ChlZ2XA4HD4PYwwODobBnAOr3Qyb3QqlUlmie3XYHWKFkkQqCdjKrKpKr9cDcFae5V+pLS0tze35kpzrzTff9Kj2AYDq1asDcIYyU6ZMwZQpU3D27Fls3rwZCxYsQL169dCmTZtin/eFt9fg7t270a1bN7fJ4C9cuODz/eUXERGBdC/DOl1DPwHn/w2JROKxn81mQ1ZWVon6tqJUvcHLRERERER3kM6du6B6tWr4Pc+E6tWqoWOBoRRUNjQaDcw2o/jYfLP6yGQx4OKVv3Hxyt/45+oZ2GxWyBXuwZErbCk4nY+Q77lAk5GRgTCls4okTKWCw+HwqIQoil6vh0NwYOn3zrldSrrak2uoGgBIA7gyq6pq1qwZ5HI5du/e7bb9+++/h0ajQeMSBNQtW7aEWq1GSkoKmjRp4vHH2zxjDRo0wPPPPw8AuHjxYomfLwmz2exRMbVr165SnatZs2Y4dOgQcnNzxW0HDhxwmxxbq9WiYcOG+P77792O/fHHH2G329G6detSXbs8seKIiIiIiKgSe2roUDw1dKi/m1HlabVamG235kcyWZ0fBDOyb2D2e6MBAHKZAj06DPBYPcxVgZSXqYAu3AqzQQa7VQK7TSi8OsnPMtLSEKtyhj1hGudk6xkZGQgNDfXp+CZNmqBzp86wWK2orbq70JX6CpscWxCEW0PVGBxVuNDQUDzxxBP46KOPoFAo0LJlS/z+++/YunUr4uPj3ZafL45Op8Po0aOxfPlypKSkoF27dpBIJLhy5Qr27duH+fPnQ61WIz4+Ht27d0f9+vUhlUqxc+dOKBQKMUgp7vnS6tixIzZv3ozPPvsMNWvWxDfffIPLly+X6lyDBw/G559/jueffx7Dhg1DTk4OVq9e7VFFNGrUKPz73//GSy+9hH79+omrqnXo0OG2htSWl8D8LkVERERERBRAtFotTNZbwZGr+kit1GLy6DcBOIfB7D20Axqt+4dqVzXDmZ8iEBRiwz9H9MhI1rg9F2jS0tLQLNz5YTf85pC19PR01K1b16fjo6Ki8Mqrr4iPN2/eXKLrC4Jwa0okSeEBU6BJvZJVZa45YcIE6PV6bN++HevXr0e1atUwadIkDBkypMTneuqppxAVFYVPPvkEn376KeRyOWrUqIGuXbuK4WnLli3x9ddf48qVK5BIJKhfvz4WLVokvuaKe760Ro4ciYyMDLz33nsAgPvvvx9Tp07F1KlTS3yuyMhILFmyBIsXL8aLL76IuLg4TJs2De+8847bfvfeey/mz5+PtWvXYvr06dDpdOjTpw/Gjx9/W/dSXhgcERERERERFUOr1SLXlI3Nvy0FAOSaMwEAdocVdao3FPczW03iMuUuruW863XMgC7CggZ3Z8BqysLRL6u5LfUdKARBQHpmJsLjnHPbhN2sPHLNb1OWCpv3yO5wwJzrrMwChICvOAoJCYFKpcLW9/b55foqlcrrkK+ixMfHIz4+HoBznqGDBw+6PS+VSjF8+HAMHz7cp3Pkt2fPHo9tvXv3Ru/evQs914QJEzxWGivJ88VZuXKl1+1arRazZs3y2F6wPwo+BoChQ4diaIGKzzZt2mDjxo1u2+655x6PY++77z7cd999hba3Xbt2Xq+5bds2j22F/TuUFQZHRERERERExWjevDka3NUAKba/nRtUQGxsLHKzDW77WSxGaILdK45cw3qUGjtkCgHaEBssSqnbc4EkJycHNpsNFrsdRpsNGSYzglRKr5P+lhfBIeDSsRBE1DJBEAK/4ig6Ohrr1q1DVlbFVxwBzuAq/yTWRGWJwREREREREVEx7rrrLixdusRt2/bt27F2zTq3bSaLEVFB7ktlK5VKyOUy2Cy35j5yfR0UFFROLS49V2XRBydOo3F4GDb/dRZyeK4CVRaKCoRqtc6ELsICqSzwgyPAGR4xvPEPu91e5GskUOcSqyzYe0RERERERKWgVqthtVlgt9sgkzk/WpmtRmi1nuFBkE4LqzlfcHTza51O57Gvv7kCope6tEO9UD3GtG6GZYeOV1jFkSsAUOucFVoSiaRSBEfkP4MGDcLVq1cLfd7bkC/yHYMjIiIiIiKiUnDNZWSyGBGkCb75tcFjjiMACA4Ohs0kEx+7QqSCqy0FgszMTABA6+hIqGQyaHRyRAdpcD299HMcCYIACbzPZ+RtXwDi5NiSSjQ5NvnHokWLYLVa/d2MKovBERERERERUSm4AqIzl06gce1WyMxNR54x22sVUVhYOK7l3qo4shqdIVIgBkdZWVlQy+VQyW4FXSEqJf7OLP38PYIgwCEIuHjFOUeURCJxVhJ5mfTaFRLlnzebwREVpUGDBv5uQpXG4IiIiIiIiKgUXMHR2x/PxMzhy/DffRuRk5ftdd6i8LAIXL6hEB9bjFJotWoolcoKa6+vcnJyoFO5tytYoUBObuptnddiNWP2e6MBAHKZAo/3jIe9iODIRSIRGBwR+RGDIyIiIiIiolJwVRYNf3g6alVrgKF9J+LFd4YVUnEUBqsxX3BkkCMsLKzC2loSBoMBQQqF2zatQg6DwVjqczocDqiUaswcvgyAs+Lor4tHC93XuRPEvx1eAiYiqhgMjoiIiIiIiErBVXEUEhwOtUqLUDirZbwFRxERETAbbg1VsxikiI2IrKCWlozZbIZMApzNcA5Nk0gAlUwGm90Ou90OWb4hbCUhk8pQp3pD8fHpi0eLnPVIAlYZEQUCBkdERERERESl4BqSZjDlAQCM5jy37fmFh4fDahZgt0ogUwiwGlWIqheYS7c7HA5km8yY/OPPAAC5VIoJbZqLz5UmOBIEARKpe0wklcrgEDwriQpWHElYcUTkVwyOiIiIiIiISkGlUkEmk4mBkStACg4O9tg3KioKAPC/9TWhUElgNQuI7B6YFUcSiQQhahX+07ndzcfAhawcAIBUKi3q0EI5HA5IJZ7HCg7PqiKPybG5qhqRXzE4IiIiIiIiKgWJRAKtRgujKRdA0RVHTZs2xfjx42EymcRj77333oprbAkolUo4IEGDsBBx2+n0TEil0lIPU3M4HJBIPCuOvE2OzYojosDC4IiIiIiIiKiUtNogGG4GRq4AydscRzKZDP369avQtpWWVqtFntXqti3PakOQRlPqcwqC4FFxJJVIIRQxVE0iEcS/7XZ7qa9dUVJSUpCVleWXa4eEhCA6OjCHPt6uK1eu4KuvvsKAAQPEyr3y8Pnnn+OXX37ByZMnkZmZiTfeeAM9e/Z022fHjh2YM2cOvvnmG4SGhpZbWwINgyMiIiIiIqJS0mq1MJkNAADjzb91Oh2sBYKXyiQkJARZRhPOZmQhLjgIGSYzkrJyvAZivnJWHLkHRxKJBIIgeAxDc4VEErHiSAj4iqOUlBQ8O2IErGazX66vUKnwwbp1VTI8unr1KtasWYN77rmnXIOjnTt3AgDuvvtu8WtyYnBERERERERUStogLQwFhqpptVq/VZ6UhfDwcJjtdkz+8WfMv7czNv91FkdSbqBFs2alPqfD4fCYH0kqlYnPFdwXACTSm4GSFAFfcZSVlQWr2Yy0hx6GLSKiQq8tT0tDxM4vkZWVVSWDo4qyZs0aSKVSXLlyhcFRAQyOiIiIiIiISkmr1cKUbQQAmCxGqFXqUk8gHShiYmIAANM7tEa9UD3GtG6GNw4eRtTN7aXhbXJs15xHgiC4zX9ks9mcz0td+1WOoWoAYIuIgDWmmr+b4bPz589j2bJlOHnyJMxmM2JiYvDII4/g6aefxmuvvYZTp05hwoQJSExMxOXLl1G3bl1Mnz4dLVq0cDvPzp078cknn+DixYvQaDRo2rQpXnjhBcTGxgJwVmStWLECBw4cgMFgQLVq1TBw4EAMHjy4yPYdOnQICQkJAIBnn31W3H7w4EEAwLVr1/D222/j119/hdVqRfPmzTFhwgQ0adJE3HfAgAHo2rUrqlWrhs2bNyM7OxsdO3bEjBkzEBl5a4L60v6/3blzJ+bOnYtp06bhscceK9U5Ah2DIyIiIiIiolLSajVIuZENADCZDdBotH5u0e2rVs0ZfCikUmjkcmh0cmRZrKhevXqpz+kMhzznOAKcoVL+SbdvBUeC+LdrG5WtadOmISwsDC+99BKCgoJw+fJlpKSkiM/fuHEDCxYsQHx8PIKDg7FhwwZMmjQJn3/+OcLDwwEAGzduxPLly/HII49gzJgxsNls+P3335GZmYnY2FhkZWVh1KhRAICxY8ciLi4OSUlJSE5OLrZ9jRo1wvTp07Fw4UK8/PLLqF27tvhcXl4exowZAwCYPn06NBoNPvzwQ4wdOxYffPAB6tSpI+67d+9eVKtWDf/+97+Rk5ODd955By+88ALWrl17W/332WefYenSpXj55ZfRp0+f2zpXIGNwREREREREVEoajQYmy3UAgNlihEaj9nOLbl9oaCg0ajWu5DmH3uVZrcg0mm4rOPK2qpqkkODIVV3kKgCRSB0MjspBZmYmkpOTMXnyZHTr1g0A0L59e7d9srOzMW/ePHF7mzZt8Mgjj2DTpk1ISEhAbm4u1qxZgwEDBmDmzJnicd27dxe//vjjj5GRkYHNmzeLr6GC1ymMTqdD3bp1AQD169d3qyTasWMHrl27ho8//hj16tUDAHTo0AGPPvooNmzYgFmzZon75uXlYcmSJQgODgYAREdHY/z48Thw4AA6d+7sW4cVsH79eqxZswZvvPGG2/1WRZW7hpKIiIiIiMiPVCoVLNZ8Q9VuY+WxQCGRSBBXvTqSc5zBkevvGjVqlPqchU2ODcBjcmzXxOK3Ko4YHJWHkJAQxMbGYsWKFfjqq69w/fp1j310Op1byBMcHIx27drhxIkTAIDjx4/DZDLhkUceKfQ6v/32G9q1a3dbwaM3R48eRb169cTQCHAOHe3WrRuOHj3qtm+7du3E0AhwBkxBQUHifZTUqlWr8P777+Ott96q8qERwOCIiIiIiIio1NRqNcxWEwDAYjVBra78FUcAEFezJpLznKvEXcl1BkdxcXGlPp8gCJCgYMWR83HBybEtFgsAQCq7GRzJHDBb/LNaWVUmkUjw9ttvo06dOli4cCEeeeQRDBs2DEeOHBH38bbkfHh4ONLS0gBAnAQ+/1xBBWVlZZXLamg5OTmI8DIReUREBLKzs922uYbV5RcWFibeR0nt3r0b9evXR6tWrUp1fGXD4IiIiIiIiKiUVCoVLDdDDbPVBLVa5ecWlY0aNWrgys3gKDk3D2EhIdBqb2/+Jo+hagWCJBdXxZErOJLKBFhvhklUtmrXro158+bh+++/x8qVK6FSqTB16lQYDM5/+8zMTI9j0tPTxcAmJCQEgHMupMKEhIQgNTW1zNuu1+uRnp7usT0tLQ16vd5tm7f9MjIyvAZPvli0aBGuXLmCGTNmiK/XqozBERERERERUSnlrziyWs1QKqtGcFS9enVkGk0wWG24mmdA9biyHWZUFLHiSH4rODKZTRV2/TuRXC5H27ZtMWzYMOTl5YlBUG5uLn7//Xdxv9zcXBw6dAjNmjUDALRo0QJqtRo7duwo9NwdO3bEoUOHcO3atVK1TaFQAADMZveqs1atWuHcuXM4f/68uM1oNOJ///sfWrdu7bbvoUOHkJubKz7+7bffkJeXh+bNm5eqTbVr10ZiYiJOnjyJl156qcoPpeTk2ERERERERKWkVCphsTqDDovNjDB1iJ9bVDZcK6ulGAy4bjQhrmGTYo4ongDB6+OClUgFh6pJZQLMlSQ4kpdy6JM/rnnmzBksW7YMvXr1QlxcHPLy8rB+/XrExsaKwxL1ej1ef/11jB49GjqdDhs2bAAADB48GIBzDqRRo0Zh+fLlsNvt6N69OwRBwKFDh9C7d280adIEQ4YMwc6dOzFmzBiMGDEC1atXx5UrV5CUlITx48cX285atWpBJpPhyy+/hEwmg1wuR5MmTdC/f3988sknmDp1KsaMGQONRoONGzfCbDZj2LBhbucICgrC888/j2HDhiE3NxfLly9Hs2bN3CbGPnXqFK5cuSJWWbnmPwoLC0Pbtm092tWgQQMsW7YM48aNw+zZszF79mxIpVWzNofBERERERERUSkplUoIggM2uw1WuwVKpdLfTSoT0dHRAIBUgwmpBhPa3HxcWhKJxGMSbAjegyNXZYnsZsWRTC7AnB3YcxyFhIRAoVIhYueXfrm+QqUSh435KiIiAuHh4Vi/fj1SU1MRFBSE1q1b49VXXxVXuYuMjMS4ceOQmJiI5ORk1KtXD2+//bbbEK+nn34aoaGh2LRpE3bu3AmtVosWLVogLCwMgLNvVq9ejRUrViAxMRFmsxmxsbEYNGiQT+0MDQ3FtGnT8OGHH+Lrr7+G3W7HwYMHERQUhFWrVuHtt9/Gm2++CZvNhmbNmmHlypWoU6eO2zm6d++O6OhovPnmm8jJyUGHDh0wY8YMt30+++wzfPXVV+Ljjz/+GADQtm1brFy50mvbGjdujKVLl2LixImYO3cuXnrpJY/Xc1XA4IiIiIiIiKiUXEGR1WaB1Vp1gqOwsDBIJRLcMBqRYTSKQcGVK1cwc8YMyGQyLFm61OewQiqVwiG4T4LtelxYcCQOVZMLMJgDOziKjo7GB+vWiZNFV7SQkBAx7PNVeHg4Zs+eXex+99xzD+65554i93n44Yfx8MMPF/p8TEyMT9cqzMCBAzFw4ECP7dWqVcO8efN8OsczzzyDZ555ptDnZ82ahVmzZhV5jv79+6N///5u21q0aIEff/zRpzZUVgyOiIiIiIiISsk1/4rVZoHNbhEfV3YymQz64GAkZefCIQhi9cjVq1eRcnOi49TU1BIFRzarGRev/I1qETWQmZuOlPQr4rXyM5lMkMkBV54kUwiwmAN/cuzo6OgShzdElUHVHIBHRERERERUAfIHR1Zb1ak4Apzz21zOzRO/BuA2CXBJJgSWy+XIMWRj9nujkXTtLD7c+Ta27XkfQCHBkeJWFZJM7oC5EgRHVHKCIMBmsxX6x+FwFH8SKnesOCIiIiIiIiolV3Bks1lgtVurTMURAOiCdbie5FzGPDg4GADcPsjb7XafzyWXy6FSqjHt6bdQLaIGhj40CT8d2Ymvf94EqVTqdi6TyQS54tZ8SFK5AKvVBrvd7hEyUfkpbthWWfjqq68wZ86cQp8fNWoU4uPjb+sa27Ztu63jicERERERERFRqbkqjGx2K2y2qhUcBQXpcMboXM1Mq9UCcA+LSlINolAoYHfYUad6QwBANZUWIUFhUHmp0DIajeLE2MCtSbJNJhOCgoJKfiMUsLp164YPPvig0OcjIyMrrjFUKAZHREREREREpSSXOz9SWW1WWKtYcKTRamG9GQ55C45KUnGkVCphtbkPN7PaLFCqVB77mkwmSOW3QimZwvm10WhkcFTFhISElHg1OKp4nOOIiIiIiIiolKpyxZFarRa/Vt0MePJXGZWk4kilUsFicV8ZzWI1Q61Se+xrNBohld8KpWSKWxVHRFTxGBwRERERERGVkqviyGazwFbF5jhS5asGct2nINwaQpb/6+Ko1WrY7FbY7bcm1DZbTW7hlIszOLq1X/6KIyKqeAyOiIiIiIiISslVcWSyGN0eVwX169cHADRp3FjcVtrJsTUaDQDAbLkV/pjMBmiDtB77Gox5YpUR4D7HERFVPM5xREREREREVEquCiOj2blsvasypyp48MEH8eCDD7ptK+3y6K45koxmA7Qa5wptJosBQV6CI1PBybE5VI3Ir1hxREREREREVEpicGRyBkdVqeLIm/zD00oSIrmCI8PNgA0ADKY86HQ6j33z8nJhswE5NxTIuaGA7OZE2QyOiPwjIOLwCxcu4PXXX8ehQ4eg0WjQr18/TJs2zet4V5fc3Fy8//772LdvHy5cuAC5XI5mzZphypQpaNasWQW2noiIiIiI7lSuoMgViFSlOY68KW1w5FoN7bPvVuGuWi0AAMfPHkT/xv099s3KyoHxmg6p53SQSAXc88wlAAyOiPzF78FRdnY2nnnmGVSvXh3Lli1Deno65s2bh8zMTCxatKjQ465cuYLNmzdj0KBBmDhxImw2GzZs2IDBgwdj06ZNDI+IiIiIiKjcuYIigykXQNWvOMofFpVkcuzo6GjUqlUbF6//hUupZwAAen0IWrdu7bGvVCZBbJMcxDZy9qnk5jgZi8VS+oZXgJSUFGRlZfnl2iEhIYiOjvbLtauy3bt348UXX8TWrVtRvXp1fzfHb/weHG3atAnZ2dnYtm0bwsPDAQAymQzTpk3D2LFjxQnZCqpRowa+++47cZI1ALj77rvRs2dPfPjhh5g3b16FtJ+IiIiIiO5cEokECoXijgmObmdVtXffXeW2TSaTISwsDBkZGW7bbTY7tHobgiOt4ja5QhLQwVFKSgpGjBgBs9nsl+urVCqsW7eO4RGVC78HR/v27UOXLl3E0AhwTsL24osvYu/evYUGR64xsvmpVCrUr18fKSkp5dZeIiIiIiKi/KRSKfYe+hJA1Q+O8lcclXai7OJYrTZIZe6hlEwe2BVHWVlZMJvN+NfkKYiqUbNCr516+RI+W7IYWVlZd0xwZDKZipzahsqW34Ojc+fOYdCgQW7blEolatWqhXPnzpXoXAaDAadOncKjjz5alk0kIiIiIiIq1Pjx43Hx4kVotVrUqVPH380pV+UdHDkcDjjsDkgKBEdSWWAHRy5RNWoirpDih0B0/vx5LFu2DCdPnoTZbEZMTAweeeQRPP3003jttddw6tQpTJgwAYmJibh8+TLq1q2L6dOno0WLFm7n2blzJz755BNcvHgRGo0GTZs2xQsvvIDY2FgAzoqsFStW4MCBAzAYDKhWrRoGDhyIwYMHF9vGQ4cOISEhAW+99RZ27NiBgwcPok2bNli8eDF27tyJrVu34uLFixAEAXfddRfGjx/vNnXN6tWr8dFHH2H16tVYsGABTp8+jbi4OEyaNAmdO3cW97PZbFi2bBm+/vpr2O129OjRA23atPFoT1ZWFpYvX459+/bBaDSifv36eO6559zONXbsWGg0GjzwwANYs2YNUlNT0b59e7zyyiswGAyYN28ejh07hmrVqmHatGlo3759if/tKpLfg6Ps7Gzo9XqP7Xq9vsTjQ5cuXQqj0YihQ4feVptkMtltHV/WXO0JtHYFIvaV79hXvmNf+Y595Tv2le/YV75jX5UM+8t37Kui5V+y/k7oK4lEIg5Tu5379NZXdrsdACCVugdHEpkAu91epfvVH6ZNm4awsDC89NJLCAoKwuXLl91G8Ny4cQMLFixAfHw8goODsWHDBkyaNAmff/65OGpo48aNWL58OR555BGMGTMGNpsNv//+OzIzMxEbG4usrCyMGjUKgDNQiYuLQ1JSEpKTk0vU1vnz56Nv374YNGgQJBIJAOfcxw899BBq1KgBq9WKb775BmPGjMFHH32EWrVqicfabDa8+uqreOKJJzBy5EisX78eM2bMwPbt2xESEgIAeOedd7BlyxbEx8ejcePG+Oabb7By5Uq3NtjtdkyePBmXLl1CQkICoqOj8cUXX2DKlClYtmyZWwD0999/Izs7G5MmTUJubi7eeustvPHGG7h+/ToeeughPPnkk1i/fj1mzpyJ7du3ex1VFSj8HhwVRhAE8cXgiy+//BLr16/HrFmzULt27VJfVyqVIiwsrNTHlydvARt5x77yHfvKd+wr37GvfMe+8h37ynfsq5Jhf/mOfeW7qtpXSqUSSoUcZosVGo2mTD475e+rvDzn6nSuCbFdpFJALpcH7Ge1yigzMxPJycmYPHkyunXrBgAelS/Z2dmYN2+euL1NmzZ45JFHsGnTJiQkJCA3Nxdr1qzBgAEDMHPmTPG47t27i19//PHHyMjIwObNm8UJpktTYXPvvfdi3LhxbttcgRTgrFbr2LEjTp06hR07diAhIUF8zmq1IiEhAV27dgUAxMXF4fHHH8cvv/yCvn37IisrC1u2bMGwYcPw7LPPAgA6d+6M+Ph4pKamiuf5+eefcfLkSSxevFg8V5cuXTBkyBCsXbvW7b5cYZErmDpz5gw+/vhjvPDCCxg4cCAAICoqCk8++SR+++03tz4LNH4PjvR6PbKzsz225+TkFDq/UUE///wzZs6ciZEjR+Kpp566rfY4HA6v7fEnmUwm9pMrgSfv2Fe+Y1/5jn3lO/aV79hXvmNf+Y59VTLsL9+xr3xX1fsqNzcXSrkCZosVWVlZHhNbl4S3vsrJyQEASApUHEEiwGAw3Nb1fKHX6++YqqaQkBDExsZixYoVyM7ORvv27RETE+O2j06ncwtDgoOD0a5dO5w4cQIAcPz4cZhMJjzyyCOFXue3335Du3btbntVsrvvvttj24ULF7By5UocO3bM7bWRlJTktp9UKkXHjh3FxzVr1oRCoRCrq86dOwez2Yz77rvP7bj7778fx44dEx8fPXoUWq1WDI1c5+7Vqxc++OADt6q4u+66SwyNAIgVUB06dPDYFujzNPs9OKpfv77HXEYWiwVJSUkecx95c+zYMYwfPx59+vTB9OnTy6RNgfoN3m63B2zbAg37ynfsK9+xr3zHvvId+8p37Cvfsa9Khv3lO/aV76pqX1mtVsjlMkglElit1jK5x/x9ZbPZAAAFB59I4Pwlf1XsU3+RSCR4++23sWrVKixcuBBGoxGNGjXC5MmTxbl9QkNDPY4LDw/HpUuXAECcXiYyMrLQ62RlZflcFFKU/AtqAc7qtIkTJyIsLAzPP/88qlWrBpVKhblz53rMh6VSqaBQKNy2yeVycb8bN24AgEdFW8Fr5uTkeGwDgIiICNhsNhiNRuh0OgDOkC0/1/Xzb3dt89dqfL6SFr9L+br33ntx4MABt3Twu+++g8ViKbZU69y5c4iPj0fbtm0xb968Eg1tIyIiIiIiopKx2WxQyOSQy+ViyFMhJBDnVaKyU7t2bcybNw/ff/89Vq5cCZVKhalTp8JgMABwDmcrKD09HREREQAgVtS4ghdvQkJC3IZ7lVbBz/vHjx9HSkoKXnrpJfTp0wetW7dGkyZNkJubW+Jzu4KvghVt6enpbo/1er3HNgBIS0uDXC6HRqMp8bUrA78HR4MHD0ZwcDASEhLw008/Ydu2bZgzZw4efvhht1TyxRdfRNOmTcXHaWlpGDlyJBQKBUaNGoWTJ0/i6NGjOHr0KP78809/3AoREREREVGVZrVaoZDLoJTLYbVaK/TaLBQoP3K5HG3btsWwYcOQl5cnBkG5ubn4/fffxf1yc3Nx6NAhcdWyFi1aQK1WY8eOHYWeu2PHjjh06BCuXbtWpm12VenkryQ6duwYrl69WuJz1a9fHyqVCnv27HHb/uOPP7o9btWqFQwGA/bv3y9uczgc2L17N1q2bFllhzn6faiaXq/H+vXr8frrr2PChAlQq9Xo378/pk2b5rZfwbLEs2fPii8I1+RVLnFxcfjhhx/Kve1ERERERER3ErPZDJVCAaVC4TEcqCy4wiFDlhx2qwRmgwx2qwSCo8wvVS5SL1+qNNc8c+YMli1bhl69eiEuLg55eXlYv349YmNjERcXB8D5ef3111/H6NGjodPpsGHDBgDOAhDAOQfSqFGjsHz5ctjtdnTv3h2CIODQoUPo3bs3mjRpgiFDhmDnzp0YM2YMRowYgerVq+PKlStISkrC+PHjS33fzZs3h1arxcKFCzFs2DCkpqZizZo1iIqKKvG5QkJCMHDgQGzYsAEqlUpcVa1gCNW1a1c0a9YMr776KsaOHSuuqpaUlFRmU+cEIr8HRwBQt25drF27tsh95s+fj/nz54uPO3XqhNOnT5d304iIiIiIiOgmi8UClUIBtVJRLvOySKXOQTEXfgtDSIwF/xzRIyNZg6BQBHQ1R0hICFQqFT5bstgv11epVG4TMfsiIiIC4eHhWL9+PVJTUxEUFITWrVvj1VdfFfs6MjIS48aNQ2JiIpKTk1GvXj28/fbb4lA1AHj66acRGhqKTZs2YefOndBqtWjRooU4X1BISAhWr16NFStWIDExEWazGbGxsT7NaVxc+9944w0sW7YM//73v1GzZk288MIL2LhxY6nON27cONjtdnz44YdwOBy47777MGbMGMyZM0fcRyaTYcmSJUhMTMSKFStgNBrRoEEDLF68GO3atbut+wlkEoEDRd3Y7fZyn6m/pGQyGcLCwpCRkcHJ4IrBvvId+8p37Cvfsa98x77yHfvKd+yrkmF/+Y595buq3lfz589HdvJlZOYZ0KJDB4wdO7bU5/LWV2azGQMGDECddhmo0TxXrDg6tTsWD/d9AsOHDy+rW/EqLCysyIDKZDLh3LlziIqKglKpdHsuJSVFnCy6ooWEhCA6OrpMz/naa6/h1KlT+OSTT8r0vBQYLBYLUlNTUb9+fajV6iL3DYiKIyIiIiIiIgp8RoMBWpUK5psrSJU1udz5EVWpdUCmEKANuTkBtyDxWBUr0ERHR5d5eEMUCBgcERERERERkU8MRiNiVSpYbDZx5a2yJJPJIJNJ4bC7T4RttyPggyMqOUEQiqzMk0ql4vBF8h8GR0REREREROQTQ14egkKqwWa3IzMvr1yuoVAq4LAVCI6sAlQqVblcj7ybNWtWuV/jq6++cptDqKBRo0YhPj6+3NtBRWNwRERERERERD7Jzc1FsEYDu92OyzfSy+UaKpUKduutKhNBAOw2FDsPC1U+3bp1wwcffFDo85GRkRXXGCoUgyMiIiIiIiLySW5uLoK1GtgdDuTm/FMu19Bo1LDnqziyW51fMziqekJCQkq8GhxVPAZHREREREREVCyr1QqD0YgQbRAEh4DsnJxyuY5Wq4XRmj84korbiajiMTgiIiIiIiKiYmVnZwMAQoK0ECAgz2CA1Wot80mrdUHByMm9NVTNZnGGSEFBQWV6ndshCIK/m0B0W0ryGmZwRERERERERMXKysoCAITqdOK27OxsRERElOl1goP1OHYsCL+nO4OivEzndl2+6/qLQqGARCKBxWLhZN1UqVksFkgkEp+CXwZHREREREREVKyMjAwAQFiwDhKJswooMzOzzIOjQYMGITY21m1bUFAQatSoUabXKQ2ZTIbQ0FCxL5RKpdgXRJWBIAiwWCzIzs5GWFgYZDJZsccwOCIiIiIiIqJiuSqOwnQ6SHArOCprTZo0QZMmTcr8vGXFFWplZmZyyBpVShKJBGFhYR4BbWEYHBEREREREVGx0tPToVGp8E9KCmJCQ8VtdxqJRILq1asjJiYGVqvV380hKjGFQuFTpZELgyMiIiIiIiIqVkZGBuQyGeIXL8Py8WOhUSrFIVt3IplMVqIP30SVFYMjIiIiIiIiKlZmZibiIiMw9fFRqBkVhXC9/o4OjojuFAyOiIiIiIiIqFgZ6emoFhaKRjcnqY4K0ZfLHEdEFFik/m4AERERERERBb6MjAyE6YLFx2HBOlYcEd0BGBwRERERERFRsbKyshAerBMfh+l0yGLFEVGVx+CIiIiIiIiIiuRwOJCVnY1QXZC4LUynQ1ZWlh9bRUQVgcERERERERERFSkvLw8OhwMhQbeCI32QFtk5ORAEwY8tI6LyxuCIiIiIiIiIipSTkwMACNZoxG16rRZ2ux0Gg8FfzSKiCsDgiIiIiIiIiIqUl5cHANBpbwVHrhDJ9RwRVU0MjoiIiIiIiKhIrqqiIJVa3KZVq92eI6KqicERERERERERFclkMgEA1CqluE2jVLo9R0RVE4MjIiIiIiIiKpLFYgEAqBQKcZtSIXd7joiqJrm/G0BERERERESBzWazAQAuXrsOpUIOCSTQa7UAAKvV6s+mEVE5Y3BERERERERERXI4HACAcYkrAAAKmQyf/OcFAIAgCH5rFxGVPwZHRERERERE5JMZg/+FuMhIZOfl4dyVa/5uDhFVAM5xREREREREREWSSp0fHedv+gwSANt+OYAX1qwDAMhkMj+2jIjKG4MjIiIiIiIiKpLi5qTYS8eORoO46nh+4KN4/dlhAAC5nANZiKoy/g8nIiIiIiKiIimVSgBAzagoaFUqaKOiYLJY3Z4joqqJFUdERERERERUJO3NFdQMZrO4zfW16zkiqpoYHBEREREREVGRgoKCAAC5RqO4Le/m167niKhqYnBERERERERERdLpdACAbINB3JZ182vXc0RUNTE4IiIiIiIioiKFhIQAALLy8gVHeXnQqNWc44ioimNwREREREREREVSKpXQarXIyMkRt2Xk5CI0NNR/jSKiCsHgiIiIiIiIiIoVHhaGtHzBUXpODsLCw/3YIiKqCAyOiIiIiIiIqFgRERG4kZUtPk7NykZkRIQfW0REFUHu7wYQERERERFR4IuIjMQPP/yAprVrAgAOnzmLx5q38HOriKi8MTgiIiIiIiKiYjVv3hw///wz1n27GwCgVqvRtGlTP7eKiMobgyMiIiIiIiIqVt++fdG3b19/N4OIKlhAzHF04cIFjBw5Eq1bt0aXLl3w+uuvw2QyFXvczp07MWHCBHTr1g2NGjXC2rVrK6C1RERERERERER3Br8HR9nZ2XjmmWeQl5eHZcuW4YUXXsCXX36Jl156qdhjd+3ahUuXLuH++++vgJYSEREREREREd1Z/D5UbdOmTcjOzsa2bdsQfnMpR5lMhmnTpmHs2LGoX79+occuXboUUqkz+9q8eXOFtJeIiIiIiIiI6E7h94qjffv2oUuXLmJoBAAPPvgglEol9u7dW+SxrtCIiIiIiIiIiIjKnt+Tl3PnznlUFSmVStSqVQvnzp3zU6uIiIiIiIiIiMjvQ9Wys7Oh1+s9tuv1emRlZfmhRc6hcoHE1Z5Aa1cgYl/5jn3lO/aV79hXvmNf+Y595Tv2Vcmwv3zHvvId+8p37CuiysHvwVFhBEGARCKp8OtKpVKEhYVV+HV94S1gI+/YV75jX/mOfeU79pXv2Fe+Y1/5jn1VMuwv37GvfMe+8h37iiiw+T040uv1yM7O9tiek5NT5MTY5cXhcHhtjz/JZDKxn+x2u7+bE9DYV75jX/mOfeU79pXv2Fe+Y1/5jn1VMuwv37GvfMe+8l0g9pVer2cFFFEBfg+O6tev7zGXkcViQVJSEgYNGuSXNgXKN62C7HZ7wLYt0LCvfMe+8h37ynfsK9+xr3zHvvId+6pk2F++Y1/5jn3lO/YVUWDz++TY9957Lw4cOICMjAxx23fffQeLxYLu3bv7sWVERERERERERHc2vwdHgwcPRnBwMBISEvDTTz9h27ZtmDNnDh5++GG3oWovvvgimjZt6nbs2bNnsWvXLuzatQsA8Pfff2PXrl3Yu3dvhd4DEREREREREVFVJBEEQfB3Iy5cuIDXX38dhw4dglqtRv/+/TFt2jSo1WpxnxkzZmDr1q04ffq0uC0xMRHLly/3OF9cXBx++OGHUrVFEAQ4HI5SHVueZDIZyzd9xL7yHfvKd+wr37GvfMe+8h37ynfsq5Jhf/mOfeU79pXvAq2vpFKpXxZpIgpkAREcERERERERERFR4PH7UDUiIiIiIiIiIgpMDI6IiIiIiIiIiMgrBkdEREREREREROQVgyMiIiIiIiIiIvKKwREREREREREREXnF4IiIiIiIiIiIiLxicERERERERERERF4xOCIiIiIiIiIiIq8YHBERERERERERkVcMjoiIiIiIiIiIyCsGR0RERERERERE5BWDIyIiIiIiIiIi8orBUYD4/vvv8dFHH5X6+J9//hlTp05Fr1690KhRI7z22mtl2Dqq6g4ePIhVq1Z5bE9PT8frr7+Of/3rX2jevDnatGnjh9aVnfT0dDRq1AhffPGFz8d88cUXaNSoEdLT093OM2vWLNx///1o1aoVHnroIaxfvx6CIIj7HDt2DC+++CJ69+6NVq1a4YEHHsD8+fORm5tbpvcU6CwWC2bOnInOnTujUaNG+OCDD3DixAmMGDECXbt2RfPmzXHffffhxRdfxPXr1/3d3FKryNcWAOTm5mLWrFno1KkT2rRpgzFjxiA5ObnM7scfbud90G63Y/Xq1Rg6dCg6d+6MDh064KmnnsL+/fvLuJUVqyJfVxaLBQsWLMBTTz2F1q1be5yjMrp8+TISExNL/b2lqr0HFuV2+6oqfl/31dNPP43nnnuuxMetW7cOAwYMQPv27dG6dWs8/PDD+PDDDz2+35N3165dQ5s2barE9yqiyoDBUYD4/vvv8cknn5T6+H379uHUqVPo0KED9Hp9GbasbGzduhWNGjXCxYsX3bZ/9NFHaNSoEZYsWeK2PS8vD02bNkWTJk3QqFGjIv8kJiaKx/3444+45557YLFYADgDkUaNGuH48eO3fQ+XL19G69atcenSpds+V6D59ddf8e6773psv379Onbu3ImIiAg0b97cDy0LTBMmTMAPP/yA8ePHY9WqVejZsyfeeOMNfPDBB+I+X3/9NS5cuIARI0bgvffew6hRo/Df//4XI0aMgMPh8F/jK9gXX3yB7du348UXX8TmzZvRr18/5OTkoF69enjppZewbt06jB8/HgcOHMCoUaPE/7t3Kl9eWwAwdepU/PDDD3j55ZexZMkSpKSkYPjw4TCZTP5peBm4nfdBk8mEd999F40bN8a8efOwePFixMTEYPjw4fjxxx/LuKWVjy+vK5PJhM8++wwqlQrt2rXzX2PLUHJyMpYvX46UlJRSHX8nvQfebl/x+3rJ5eTkoH///njrrbewYsUK3HfffXj99de9/jxGnubPnw+tVuvvZhDdMeT+bgCVjRdeeAEzZ84E4AxLAo3rh9DDhw+jTp064vYjR45Ao9Hg8OHDbvv/8ccfsNvteOGFF9C2bVtx+/jx49G2bVuMGDFC3FatWjUAgCAIWLJkCYYPHw6lUlnm91CjRg088MADSExMxIIFC8r8/IGoUaNG+OWXXwAAiYmJOH36tJ9b5H/Xrl3D77//jjfeeAODBg0CAHTp0gV//fUXdu7cieHDhwMA4uPjER4eLh7XqVMnREZGIiEhAb///js6duzol/ZXtHPnziE6OhqPPPKIuC0qKgpdunQRH3fs2BGxsbEYMWIETpw44fZ//k7i62vrjz/+wJ49e/Dee++he/fuAICGDRvigQcewNatWzFkyBC/3YO/qNVq7N69GyEhIeK2e+65BxcvXsS6detw//33+7F1/uXr60qv1+PXX3+FRCLBF198gf/973/+bPZtEwThtgOLO+U9sCz6qkuXLvy+XkKTJk1ye3z33XfjypUr2Lp1K8aMGeOnVlUO+/fvx/79+/Hcc8/hzTff9HdziO4IrDiqQGfOnEF8fDw6deqEVq1a4cEHH8Tq1asxY8YMbN26FWfOnBGraGbMmAEAmDFjBvr374+9e/eif//+aNGiBQYOHIijR4+6nVsqDex/ylq1aiEqKsojIDp8+DAee+wxHDt2DFar1W27XC7H4MGD0bp1a/GPUqlEZGSk2zZXcHTgwAGcO3cOjz32WLndx6BBg/DVV18hLS2t3K5RUps2bRKHHzzzzDP4448/3IY2bNu2DUOGDEHHjh3RoUMHPP300zh27Jh4fGJiIpYvXw6DwSC+/p5++mkAgf+6Ks6nn36KHj16iH2TlJTk9nxxfeON63UaHBzstl2v17uVl+cPjVyaNm0KAKX+ja4/FPX6cg2J8fbn8uXL6NGjBzZs2ICrV6+6bfcmNDQUAGCz2Srw7krPn6+tvXv3Qq/X49577xW3Va9eHW3btsXevXtv99bKVXm9D8pkMrfQCAAkEgkaN25cqf6/+fN1BTj7rLLK/zp55JFH0KJFC4waNQoA8Pjjj4uvK5czZ87gqaeeQosWLdCrVy9s27YNzz33nPj+B1T+98DClEdfeVPZvq8XprifswpKTExEmzZt8Ndff2HIkCFo1aoV+vfvj59++qnYa4WFhbn9PFxZ+NJH27Ztw4ABA9CiRQt06tQJ8fHx4hBr188TJ0+exKhRo9C6dWv07t0b27Zt87iW1WrFnDlzMGHCBPE1RkTljxVHFWjs2LGIiIjA3LlzodPpkJSUhGvXriEhIQHp6ek4f/48Fi1aBMD9Q2dqaipmz56NCRMmQK/XY/Xq1Rg5ciS+/fZbRERE+Ot2SqxNmzZuwdH169eRnJyMp59+Gp999hlOnTqFli1bAnAGR02aNClRCeq2bdvQoUMHrx/Ys7KyxKEdoaGhePLJJxEfH++2z/fff4/ly5fj3Llz0Ov16N27N6ZNm4agoCBxn44dOyIkJARffvklnn322RL2QNnbvXs3XnnlFfzrX//Cgw8+iFOnTmHq1Klu+1y+fBkDBgxArVq1YLFYsGPHDjz11FP473//i7p16+Jf//oXrl27hh07dmD9+vUAAJ1O54/bKVM//vgjXn75ZQwcOBAPPfQQTpw4gSlTprjtU1zfeFOzZk107doVq1atQp06dVC9enXs378f3333XbFzi/3+++8AgPr165fNTZaz4l5f9913HzZv3ux2zBtvvIFLly4hJCQEy5cvx6pVq3D48GEsX74cABAdHS3ua7fbYbfbcenSJSxcuBDNmjWrFL+V9vdr69y5c6hbt67Hh/wGDRoEfJVIRb4POhwOHDlypNL8f/P366oqSElJwdy5czF27Fjo9XpcvnwZb7zxBubNm4d69eqJ+5lMJowYMQJ6vR4LFy4E4Pywn52d7VYVXZWVV19V1u/rhfHl5yxvrFYrpk+fjmHDhiEhIQHvvfceJk6ciB9++AFhYWFu+9psNpjNZvz222/Ytm0bxo8fX163Uy586aM1a9Zg4cKFePzxxzF58mRYrVYcOHAA6enpiIuLE/ebPn06nnjiCQwfPhybN2/GjBkz0Lx5czRo0EDcZ8OGDZDJZBgyZAi2b99eYfdJdKdjcFRB0tPTcenSJbz44ovo0aMHAKBz587i8+Hh4bhy5Qpat27tcWxmZiaWLl0qlgB36NAB3bt3x/r16z1+qAxk7dq1w3fffYfMzEyEhobi8OHDiI6ORr169dCsWTMcPnwYLVu2hMPhwNGjR/H444+X6Pz79+8v9JhXX30Vjz76KN555x18++23WLRoERo1aiT+xn737t0YP348+vTpg8mTJ+Py5ct46623cOHCBbc5IKRSKVq1aoVffvklIIKjlStXonPnznj99dcBAN26dYPZbBY/pANw+wHE4XCga9euOH78OLZu3YopU6agWrVqqFatGqRSqdfXX2W1cuVKtG/fHvPmzQPg7Buj0eg2d0BxfVOY5cuXY/LkyXj44YcBOH9LP336dAwYMKDQY7Kzs7FkyRJ06dIFTZo0uc27qxjFvb7Cw8PdPtyvXbsWJ0+exLp16xAcHIymTZsiMjISSqXS62tr6NChYpjcvHlzvPfee5DLA/9tyd+vrezsbI/qEcBZQZKVlXW7t1duKvp9cOPGjbhw4UKlCUf8/bqqCrKysrBmzRrxl1Cuoft33XUXWrRoIe63ZcsW3LhxAx9//DFq1qwJwFkR+uCDD94xwVF59VVl/b5eGF9+zvLGarVi2rRp4nDiWrVqoXfv3ti3bx8effRRcb9//vkHvXv3Fh+PHTs2IH6+LIni+ignJwfLly/H//3f/7l9P+7Vq5fHuZ566ik89dRTAIBWrVphz549+Pbbb8Xg6Pr163jnnXfwzjvvQCaTlfetEVE+VbMGNwCFhYUhLi4OixcvxtatW3Ht2jWfjw0ODnYbN67X69G5c2eP4WqBrm3bthAEQfyB4siRI+IKJW3atMGhQ4cAAH///Tfy8vJKNDlnSkoKrl+/joYNG3p9vnfv3pgwYQLuvvtuzJo1C7Gxsfjmm2/E55cvX44WLVpg6dKl6N69O5566im89tpr2L9/v8ecUU2aNMEff/xRonsvD3a7HadOnRI/gLn07NnT7fG5c+cwbtw43H333WjSpAmaNWuGCxcueExUXpXY7XacPHkSDzzwgNv2Bx980O1xafpGEATMnDkTFy9exKJFi7Bx40aMHz8eS5YswWeffVZoe6ZNmwaj0Yi5c+fe9v1VBF9fXy779u3DW2+9hRkzZqBTp04+XWPu3Ln49NNPsXDhQpjNZgwfPjzgV50LlNdWYUOKAnmoUUW+D/76669YuHAhRowYgQ4dOtxu08tdoLyuKruwsDAxCCnKiRMn0KhRIzEIAZwf7O+6667ybF5AKa++qozf1wtT0vfB/KRSqdv3rNq1a0OhUHisMhcbG4vPP/8cGzZswIQJE/D+++9j2bJlZXMDFcCXPjpy5AiMRqNPvxC+5557xK91Oh1iY2Pd3isWLFiArl27uvUtEVWMyvsrgEpGIpFgzZo1WLp0KV577TUYDAY0a9YMM2fOLPaHWm9DryIiIirdB/8mTZqIE2H36NEDhw8fRv/+/QE4g6Mvv/wSAMQAqSSlzampqQC89xXg/kYklUpRr1498Y0oLy8Pp06dwr///W+3Y/r06YMXXngBv//+u9sH4bCwMGRmZsJqtUKhUPjcxrKWnp4Om83mcc/5h23k5uZixIgRCA8Px4wZM1C9enWoVCq89NJLMJvNFd3kClNY30RGRopfl7Zv9uzZg127dmH79u1o3LgxAOcQxuzsbCxYsACDBg3ymBfj5Zdfxm+//Yb169e7lWQHMl9eXy4XLlzA1KlT8eijjxY750V+ruEQrVq1QpcuXXD//fdj8+bNGDly5O01vhwFwmtLr9fj6tWrHsdnZ2cH5KqaLhX1PvjXX38hISEBvXr1wvTp08uq+eUqEF5XVYGvw/dTUlIKfU1V9vl4fFVefVUZv68XpiTvgwWp1WqPhVoUCoXH/1WlUilWeHXq1AlarRaLFi3CkCFDEBUVdZt3UP586aPMzEwA7kPVC1OwmlahUIgTtx85cgTffPMNPv30U2RnZwMAjEYjAOfP8hqNBhqNptT3QkRFY3BUgerVq4dly5bBarXiyJEjWLx4McaMGYN9+/YVeVx6errHtrS0tErxhpKfQqFAixYtcPjwYRiNRvz111+YNWsWAGdwdOPGDSQlJeHw4cPiZNq+cr0RF7aamrc3IoPBAMBZQisIgtsP6AAgl8sRGhrqMfRDpVKJ1/RncBQeHg65XO7x+sg/cffRo0dx7do1cZlql5ycHHFS8aqosL65ceOG+HVp++bs2bOQyWRuE4cCzmB0/fr1yMzMdPsBasGCBdi+fTtWrFjh0293A4Uvry/A2V8JCQmoU6cOZs+eXerrRUVFITo6Gv/880+pz1ERAuG1Vb9+ffzyyy8QBMGtwujs2bMBP59Peb8PJiUlYdSoUWjatCkWLFgQ0BVY+QXC66oq8PXfOzo6GqdOnfLYnpaW5jHJelVVEX1VWb6vF8bX98Gy1KxZM9jtdiQnJ1eKn/N96SPXBNYpKSm39bPnhQsXYLVavS6C06tXLzz00ENYsmRJqc9PREWrGr9iqmQUCgU6duyI0aNHIzc3FykpKV5/C+GSk5OD/fv3uz0+cOAAWrVqVVFNLjNt27bFiRMncOjQIchkMnGul+joaMTFxeHw4cM4cuRIiYapARB/eHH9BqIkgoODIZFIPH4QsNlsyMzM9PjBKCsrCwqFwu8TSLv6b/fu3W7bv//+e/Frk8kEAG4B1+HDh8VVLFzy/0anKpDJZGjatCm+++47t+35hyf62jcFxcXFiaXZ+Z04cQJardZt0svVq1dj3bp1eP3118V5DioLX15fDocDU6dOFecvKCy49UVycjKuXbvmNhwiEAXCa6t79+7Izs52W6Hn6tWrOHz4cKV5nZXH+2BqaipGjBiByMhIrFix4rZejxUtEF5XVZGrrwq+rpo3b47Tp0/j0qVL4rakpCScOXOmQtsXSMqjryrL9/XC+PI+WNYOHToEiUSCGjVqlNs1ypIvfdSmTRtoNBps2bLltq7VrVs3bNiwwe2Pa6Gbd955B+PGjbut8xNR0VhxVEH++usvvPnmm3jooYdQs2ZN5Obm4t1330VcXBxq1aqF+vXrY8uWLdixYwdq166NsLAw8U0jNDQU//nPfzBx4kQEBwdj9erVAIBnnnlGPH9ycjKOHz8OwFm2mZSUhF27dgFwDrkKFG3btsWqVauwceNGtGjRwu0H4DZt2mDXrl1ITk4ucXBUs2ZNKBSKQpf6LkpQUBCaNGmCr7/+GsOHDxe3f/vtt7DZbGjfvr3b/pcvXy509ZqKNnbsWCQkJOCll15Cnz598Oeff4orTLgmu9ZqtZg9ezZGjx6N69evY/ny5YiJiXE7T/369WGz2bB+/Xq0adMGOp1OLDd3vY7Onj0Lu90uPm7RokVAD7saM2YMEhISMHPmTHGFoh07dojP+9o3BXXv3h1xcXGYNGkSxo0bh5iYGBw4cACffPIJRowYIf4W98svv8SiRYvQr18/1K1b120uFteE5IGuuNfX6tWrsXfvXrzwwgu4evWq2/Cppk2bFvrBfdasWQgLC0OLFi2g0+lw4cIFrFu3DpGRkSWeFN8f/P3aatWqFe677z785z//wYwZM6DT6fD2228jLi7O629iA0V5vg+aTCaMGjUKaWlpmDFjBs6ePet27cow8b+/X1cAsHfvXhiNRpw4cQKAc6W3oKAgNGjQwG1Vo8qiTp06kMlk2LJlC2QyGeRyOVq0aIFBgwZh1apVGDNmDCZOnAhBEJCYmIjIyEiPSpzK+h5YUrfbV5X9+3phinsfLK2cnBzEx8fjkUceQe3atWGz2XDgwAFs3LgR//d//+dRBR/Iiuuj4OBgjBs3DosWLYLD4UCvXr3gcDhw8OBB9OvXz20y9qJERUV5VGG5gvO2bdtWmcpJokDF4KiCREVFITIyEu+++y6uX7+O4OBgtG/fHgsXLoRMJsPjjz+OY8eOYc6cOcjMzMRjjz2G+fPni8dOmzYNCxYsQFJSEu666y6sXbvW7U3l4MGDmDlzpvj4p59+En8bffr06Yq92SK0bdsWUqkUe/fuFX9LkP+5OXPmiF+XhFKpRPPmzXHy5MlStWv8+PEYN24cpkyZggEDBuDSpUtYvHgxunTp4jHR7/Hjx0scbJWXnj174tVXX8W7776L//73v2jVqhVeeeUVxMfHQ6fTITIyEm+//TYWLFggDid69dVXsWbNGrfz3H///XjyySfx3nvvIS0tDR06dMDGjRsBAJMmTXLb1/V43rx5GDhwYMXcaCn07NkTs2fPxqpVq/DVV1+hVatWeOuttzB48GAA8LlvCgoKCsL69euxZMkSLF68GFlZWahRowamTJniFub+/PPPAICvvvoKX331lds5xo8fjwkTJpTxHZe94l5fBw4cAAC8+eabHsfu3r270N+YtmzZEp9++ik+/vhjWCwWxMbGonv37hgzZkylqH7w92sLAN566y28+eabmD17NqxWKzp16oTExESo1epyu+/bVZ7vgzdu3MBff/0FAF5/6xxI74OFCYTX1ezZs90qmF588UUAled7VkHh4eGYNWsW1qxZg//+97+w2Ww4ffo01Go11q1bh1deeQVTp05FTEwMxo0bhy1btngMba+s74Eldbt9Vdm/rxemuPfB0lKpVKhbty4++OADXL9+HWq1GrVq1cLs2bMr3WqHvvRRfHw8wsPD8cEHH2Dr1q0ICgpCmzZtfJ5ri4gCgEAB7YUXXhD69evn72aUqf79+wsNGzYUdu/e7bb95MmTQsOGDYWOHTsKDofD67H333+/MHv2bK/PrVu3TujWrZvbsQcOHBAaNmwoHDt2zG3f0aNHC0OHDnXb9u233wqPPvqo0KxZM6FLly7Cq6++KuTm5rrtk5KSIjRu3Fj45ZdffL7fivbpp58KDRs2FC5duuTvplAVxNcXVbSq+D5IgSc9PV1o27atkJiY6O+mBLw7va/4Plg89hFR1cOKI6pwrtXTCmratGmxvxX+4YcfCn3u0UcfxeLFi/H777+LK/R06tTJ6znfffddj20PPPCAx1LIBX399deoUaMGOnfuXOR+FSUzMxPLly9H586dERQUhOPHj2PVqlXo2bNnpRkfT4GLry8iqqree+89REZGIi4uDqmpqVi3bh0cDgcGDRrk76YFnDu5r/g+WDz2EdGdgcERVRnh4eEYMmQI1q1bV+zSzqVht9uxceNGjBs3LmBW6pHL5bh06RK++uorZGdnIywsDI8++iimTZvm76ZRFcDXFxFVVTKZDKtWrcK1a9cgk8nQqlUrrF+/HrGxsf5uWsC5k/uK74PFYx8R3RkkgiAI/m4EUVlJT0/Hxx9/jNGjR5f5ijpXr17F9u3bMXr06NuaEJGIiIiIiIiosmBwREREREREREREXrFsgoiIiIiIiIiIvGJwREREREREREREXjE4IiIiIiIiIiIirxgcERERERERERGRVwyOiIiIiIiIiIjIKwZHRERERERERETkFYMjIiIiIiIiIiLyisERERERERERERF5xeCIiIiIiIiIiIi8YnBEREREREREREReMTgiIiIiIiIiIiKvGBwREREREREREZFXDI6IiIiIiIiIiMgrBkdEREREREREROQVgyMiIiIiIiIiIvKKwREREREREREREXnF4IiIiKiS+eKLL9CoUSM0atQIBw8e9HheEAQ88MADaNSoEZ5++ukyu26PHj0wY8YM8fHBgwcLbQMRERERVQ0MjoiIiCqpoKAgfP755x7bf/31VyQlJSEoKKhcr9+sWTNs3rwZzZo1K9frEBEREZH/MDgiIiKqpB566CF8++23yM3Nddv++eefo02bNqhevXq5Xl+n06F169bQ6XTleh0iIiIi8h8GR0RERJVUv379AAA7duwQt+Xk5ODbb7/FoEGDPPa3WCxYsWIF+vTpg+bNm6Nz586YOXMm0tPT3fazWq1YsGABunbtilatWmHIkCE4duyYx/m8DVU7fvw4Jk+ejB49eqBly5bo0aMHpkyZguTk5LK6bSIiIiKqQHJ/N4CIiIhKR6fT4cEHH8SWLVswePBgAM4QSSqVom/fvli/fr24r8PhQEJCAg4dOoSRI0eibdu2SE5ORmJiIo4dO4YtW7ZArVYDAF5++WVs27YNI0aMQNeuXXHmzBmMHz8eeXl5xbYpOTkZdevWRb9+/RASEoLU1FR88sknePzxx/HVV18hPDy8fDqDiIiIiMoFgyMiIqJKbNCgQRg2bBjOnDmDu+66C1u2bEGfPn08ho99/fXX+Omnn5CYmIjevXuL2xs3bozHH38cX3zxBZ588kmcO3cOW7duxbPPPot///vfAICuXbsiIiIC06ZNK7Y9ffr0QZ8+fcTHdrsd9913H7p27YodO3Zg2LBhZXTnRERERFQROFSNiIioEuvYsSNq1aqFLVu24PTp0zh+/LjXYWo//vgj9Ho97r//fthsNvFPkyZNEBUVhV9//RUAxGFnDz/8sNvxffv2hVxe/O+b8vLysHDhQjzwwANo2rQpmjZtijZt2sBgMODcuXNlcMdEREREVJFYcURERFSJSSQSDBw4EBs3boTZbEadOnXQvn17j/3S0tKQnZ2N5s2bez1PRkYGACAzMxMAEBUV5fa8XC5HaGhose2ZOnUqDhw4gISEBLRo0QJBQUGQSCQYPXo0zGZzyW6OiIiIiPyOwREREVElN3DgQCxbtgybNm3C5MmTve4TFhaG0NBQrFmzxuvzQUFBACCGQ6mpqYiJiRGft9lsYqhUmJycHOzZswfjx4/H6NGjxe0WiwVZWVkluCMiIiIiChQMjoiIiCq5mJgYjBw5EufPn8eAAQO87nPffffhq6++gsPhQKtWrQo9V6dOnQAAX375pVt10tdffw2bzVZkOyQSCQRBgFKpdNv+2WefwW63+3g3RERERBRIGBwRERFVAcVNXN2vXz98+eWXGD16NJ5++mm0bNkSCoUC165dw8GDB9GzZ0888MADqF+/Ph555BGsX78ecrkcd999N86cOYO1a9d6TLhdkE6nQ4cOHbB27VqEhYUhLi4Ov/76Kz7//HPo9fqyvF0iIiIiqiAMjoiIiO4AMpkMK1euxIYNG7B9+3a89957kMlkqFatGjp06ICGDRuK+86dOxeRkZHYunUrNm7ciCZNmiAxMRFTpkwp9jpvvfUW5s6di4ULF8Jms6Ft27Z4//338dxzz5Xn7RERERFROZEIgiD4uxFERERERERERBR4pP5uABERERERERERBSYGR0RERERERERE5BWDIyIiIiIiIiIi8orBERERERERERERecXgiIiIiIiIiIiIvGJwREREREREREREXjE4IiIiIiIiIiIirxgcERERERERERGRV3J/NyDQCIIAh8Ph72YUSSqVBnwbKwP2Y9lgP5YN9mPZYD+WDfZj2WA/lg32Y9lgP5YN9uPtC/Q+lEqlkEgk/m4GUUBhcFSAw+FARkaGv5tRKJlMhrCwMGRnZ8Nut/u7OZUW+7FssB/LBvuxbLAfywb7sWywH8sG+7FssB/LBvvx9lWGPgwLC4NMJvN3M4gCCoeqERERERERERGRVwyOiIiIiIiIiIjIKwZHRERERERERETkFYMjIiIiIiIiIiLyipNjExEREREREZWQ3W6H1Wr1dzOISkyhUJRoEngGR0REREREREQ+EgQBV69eRWZmJgRB8HdziEpMIpEgNDQUsbGxkEgkxe7v9+Don3/+wdq1a/HHH3/gzJkzqFevHnbs2OHTsVu3bsW7776L5ORk1K5dG+PGjUPfvn3LucVERERERER0p7p69SoyMjKg1+uhVCp9+uBNFCgEQYDFYkFGRgYAoHr16sUe4/fg6MyZM9i7dy9atWoFh8Phc2K7a9cuzJgxA6NHj0bXrl3x/fffY/LkyQgODsY999xTzq0mIiIiIiKiO43dbkdmZib0ej2Cg4P93RyiUlGpVACAzMxMxMTEFDtsze/BUY8ePdCrVy8AwIwZM3DixAmfjnv77bfRp08fTJ06FQDQuXNnXLhwAcuWLWNwRERERERERGXOarVCEAQolUp/N4XotiiVSgiCAKvVWmxw5PdV1aTSkjfh0qVLOH/+PPr37++2vX///jh27BjS09PLqnlEREREREREbjg8jSq7kryG/R4clcb58+cBAPXq1XPbXr9+fQiCID5PRERERERERESl5/ehaqWRlZUFANDr9W7bQ0JC3J4vrZIsS3f16lXk5uZ6bDcYDNBqtR7bdTodYmNj3bZZLBZ88sknuHjxIqpXr47MzEwoFArUrl0bjRs3hlx+659JKpWiWrVqHvdeVFu8XbMk+1aEsujHws7j7Rwl7ceSXK+o/cvbjz/+iGPHjqF27dpITU2FRqNBnTp1kJycjLZt2/rUzry8PBw4cABXr15FeHg4cnNzER4eDqVSiZiYGLcqwcL60W6348iRI8jMzER0dDSysrJgNptRs2ZN1K1b16O0N9D6cffu3Thx4oRHP165cgVt2rTxqZ3/+9//8OOPP6JatWrIyMiARqNBdHQ0cnNz0a1bN7eEv6jX4x9//IHU1FS3fmzQoAHq1KnjsW+g9WNFMBqNuHTpEurUqSO+rkryPdwbm82G8+fPu825p1AoULdu3Tvit4uu/rvdfgScFcIGg0F8HB0djbCwsNs+b2VQlv14J2M/lg32Y9lgP94+9mHlsGPHDsyZMwdKpRKffvqpx8+RY8eORWZmJj755JMSnfebb75Beno6hgwZUpbNvS2rV6/GmjVr8M033yA0NNTfzQlYlTI4cin4A7zrh/zb+cFeKpX6/ENtZmYmhg8fDofD4fP5ZTIZdu3a5fai3LhxIz766KMSnyN/O4tqS8FrlmTfilBW/VjS85SkH0t6PX/0IwC8+eabhd7/2rVrPbZ5a+fixYuxa9cun6/prR+/+OILzJs3z+v+w4YNw4QJE8THgdiPCxYsKNHSqt7a+eabb8JsNnvdf/PmzYWeI38/fvPNN3jppZe8nmPOnDno06eP+DgQ+7EoWVlZ+OuvvwA4g61mzZqV6jyrV6/GZ599hhEjRmDs2LEAPH+pUFIfffQRli5d6rH9nXfeQceOHW/r3JXJ7fZjcnIyRo4c6batXoMG2FzCHzIru9vtR3JiP5YN9mPZYD/ePvZh5WCxWLBq1SrMnj27TM73zTff4Pz58wEVHJFvKmVwlL+yKDIyUtyenZ0N4Pa+ETkcDvE8vnj//fdLXCkjCIK49B0A9O7dG1evXkVSUhIsFguMRiNq1qyJmjVromXLllAoFOK+rsoEqVTqdo6i2uLtmiXZtyKURT8Wdp6iKo587ceSXK+o/ctbk8aNcfLPP/FEo3oQIIFCKsH2MxeQZ7Nj8eLF4uz5RbUzJycHweow3HvXY9BrImCy5iJYHYoN++dh8ODB6Natm7hvYf2Yv8IAACAR0PCeNPz9UyTMZnPA9+Ojjz6Kbdu2AQCC5DLk2ewAgDZt2mDkyJEe4bS3dk6fPh27d+9GVFQUTp48Cb1ej+bNmyMvLw89evTwWnFUsB+PHj0KuQK4655UqHR2WE1SOGwSnPk5CkeOHEGnTp3c2hFo/ViUtxYtwjfffis+fv/99xEXF1fi87juKT09HdnZ2dDr9cjOzobdbi912/766y8IWi1S/jXYuUEQELPhfZw8eRJ33XVXqc9bWchksjLpx7NnzwIA0h4eAFt4OHS/HkTyxfMB9TosT2XVj3c69mPZYD+WDfbj7asMfajX61kRdVOXLl3w7bff4qmnnkLDhg393ZwyZTKZPD4bUeEqZXDkmtvo/PnzqF+/vrj93LlzkEgkHnMflVRJvolFR0cjOjr6ts4vk8kwYsQIn46VyWQICwtDRkaGx3mKasvt7FsRyqIfS3Ke0vRjSa/nj36c9cormDZ1Kn64dA2LunfGe8f+hFUixZtvvoEmTZp4PaZgO2NjY3FM8icaV2uHGH1NZJnScDnd+QGwVatWbv+/CuvHe++9F+vWrYEs+AaqNcyFWmdH+mU1ZDIp+vbtG/Cvx+eeew7PPPMM1qxZg6+++gp169TB3DfeKLIasWA7u3btiq5du/p0vcL6MSQkBIIAqPU2SCSAKsgOXYQVf/8PCAsLC/h+LMo///yD9g3vwvAHH8C4xBX4559/UK1atRKfx3Vfdrvd69elkXT5MkwRkbCFhEKWlwuJxQKEheHatWsB14/l6Xb70bVQhal2HQhqNcy1a8N86iRMJpPbL0SqutvtR3JiP5YN9mPZYD/ePvZh5TB06FD89ddfeOedd/D2228Xup8gCNiyZQu2bduGpKQkKJVKtG/fHhMmTBB/MTh27FgcPnwYANx++Xnw4EE8++yzCAsLw5IlS8TtTz75JM6dO4f3338fTZs2BeCclmPGjBn46KOP0KBBAwDOX7SuXr0af/75J+x2Oxo2bIhnn33WbaV119C7t99+G9999x3+97//ITMzE/v27fN6PxcvXsTkyZMRFhaGRYsWITw8vJQ9WHVUysmxa9asiXr16mHnzp1u23fs2IGWLVvyH5buWHq9HvPmz4dDqcSzX/+AX5KvYcbMmWjZsqXP52jbti1yjBl467txuJx5DlsOLcf6/XOhUqoKDZ8K0mq1GDz4SWRe0eCvPVFwOCS4eioUffs+hJiYmNLeXoVSq9XiG06Tpk39Mi9LSEgI7DbgyPZYHN4WiyP/rQYIgN16q/Kysrp+/Tqa16mNZrVrQSGX49q1a6U6T0mGFPoqKSkJmktJUKRcR+ju7xCz8QNYg3RcsbOEMjMzAZkMws3f5jk0zsrPklT1EhERkf8EBQVh+PDhOHDgAH7//fdC95s3bx6WLFmCDh06YMGCBZg+fTouXLiAUaNGIS0tDYCzGr9ly5aIiIjAmjVrxD8A0KFDBxw9ehQ2mw0AkJaWhnPnzkGlUuHXX38Vr/Pbb78hPDxcDI0OHz6McePGITc3F//5z38wZ84caLVaTJs2Dd99951HO19//XXI5XK8+uqrmDdvnttcwi6HDx9GfHw8GjRogBUrVjBbuMnvwZHRaMSuXbuwa9cuJCcnIzc3V3zs+iH9xRdfFFNGl4kTJ+Lrr7/GkiVLcPDgQbzxxhv4+eefMXHiRH/cBlHAiIiIwL/+7/8AAE2bNEGXLl1KdHzLli0RFhqGljXuQY3Q+hjYdhxCNOG4p1u3EpVz9ujRAwBQq00m7DYJrGbnsMzKxDXsy1/j8K1WKwCgUfdUtO5/DU16pCInTeH2XGVksViQkZmJmLAwSKVSRIWG4saNG6U6l2tOp7L8raXd4UBO6zawRscgs+cDuP70s7BqtMjIzCyza9wJsrKyINFqgZvDMu03hwzf7gIWREREVHEGDhyIuLg4LF++3Osv7I4fP47t27dj3LhxmDRpEjp37owHH3wQy5YtQ15enjiBdr169RAcHAylUokWLVqIfwCgY8eOMBgMOH78OABnQKTVatG/f3+34OjXX39Fhw4dxMfvvPMOgoODsXLlSvTq1Qvdu3fHkiVLUK9ePSxbtsyjvR06dMDMmTPRpUsX9OjRw2NI4tdff42JEyeiT58+ePPNN6FWq8umE6sAvwdHaWlpmDRpEiZNmoRff/0VV69eFR+fOXMGgPODQcEPBX379sW8efOwa9cujBw5Ej///DOWLFniVpJGdKdyTYKsCw4u8bEymQwP9XsIp6//DgEC0vOuIcuYjn79HipxG+rWqwNzrhy5N5TQBmnchpZWBl27dsWaNWswdOhQv1zf9WZ1em8UAAmungrGke2xbs9VRq45bqJCnIFcpD5Y/G1USbmCo5JMrl8UQRBgMhhgi4yCoFTCFhYOa0w1OLQaZLJSpkSys7Ph0GjEx66KIwZHRERElYdCocBzzz2HU6dO4fvvv/d4/ueff4ZEIkGfPn1gs9nEPxEREbjrrrvE4WlFadmyJVQqFX777TcAzoCoXbt26NKlC44fPw6TyYTr16/j0qVLYnBkNBpx8uRJ9OjRw20+W5lMhr59+yIlJQX//POP23Xuv//+Qtvw/vvvY86cORg3bhymTp3qtpI0BcAcRzVq1MDp06eL3Gf+/PmYP3++x/bHHnsMjz32WHk1jajSclVv5OXklOr43r1746OPPsKxyz/j1NXfUKtmbTRu3LjE52nerAW+/PIiAKB9+6aV7huwRCIp1YTNZcW10lhMgxxAKqB6sxwotTZcPxNc6lXIAoErOAjV6Zx/BwWVOkxw/VKhrIIjs9kMweGAQ+UezAlKFfLyDIUcRd5kZ2fDli/gdGjU4nYiIiKqPFyfDVatWuURvqSnp0MQBPTt29frsb78LK1SqdCyZUv89ttvGD16NH777Tc8/fTTaNu2Lex2O44ePYqUlBQAEFe4zc7OhiAIbotlubi2Ffz50tu+Lrt27UJUVBQeeOCBYtt7J/J7cEREZc81zLO0VRzR0dFo3KgJjl3+CWdS/8DjTwz0WEnMF4MGDYJer4fD4WA1YCnExMSgb98++PrrXbh+9lb1WJ++fSrNXFHeuIYABmud1Sh6rRbX0ku30pbt5op3rjHxt8toNAIAhAKTNzuUSphMxjK5xp0iKycHVtWtiiNBqQKkUuSUMtAmIiIi/5BIJBg/fjwmTJiArVu3uj0XEhICiUSCd999F0ql0uNYXxfEaN++Pd577z2cPHkSKSkp6NixI4KCgtC0aVMcPHgQN27cQK1atcSfgfV6PaRSqdfpDlzbXKMw8t9HYZYuXYqXXnoJzz33HJYvX47Y2Fif2n2nYHBEVAW5xuveToVPq9YtsXnzZufXrVqV6hwxMTF+G+ZVVYwfPwEPPdRPrKyRSqWVbshfQQaDs3Lnalo6QoKCnHMKlTJMcM31VFZzPlksFgCAUGCyREEuh60SzyvlD2np6YBcAYnFcmt1OpWKwREREVEl1LFjR3Ts2BFr1651+wXmPffcgw0bNiA1NRW9evUq8hwKhQImk6nQ869cuRLvvvsuoqOjUadOHQDOeYl++uknpKWluVU7aTQaNGvWDHv27MHEiRPFaRwcDgd27dqF6Oho1KpVy+f7i42Nxbvvvovx48eL4VFJjq/qGBwRVUGPPfYYatasidq1a5f6HK5v1gBu6zx0e6RSqbhyRFXh+oHh+ZXvYfn4sfjj/HlkGkpXzeM6l8lkLpO2uSqXhAKTJUImY3BUQmmpqdDm5SE35Tr0B36B+uIFICwceXl5/m4aERERlcL48ePxzDPPICMjA/Xq1QPg/AXzgAEDMGfOHJw6dQpt2rSBWq1GWloa/vjjD9SvXx+DBg0CADRo0AB79uzBli1b0LhxY0ilUnHV5saNG0Ov1+PgwYPo37+/eM2OHTti3bp14tf5JSQkYMKECUhISMBTTz0FhUKBLVu24Ny5c5gzZ06JR0xERkZi1apVmDhxIsaMGYPExMRK/wvbssLgiKgKCg8Pv+0VzPKXdupuzkVDVBbMZjMkAN6bPBE1o6PQrUVz7Pi18CVei2K6ObTMaCib+Ydcq2/IMzIg3Cy3tsZUg8RoKrN5lMra/Hnz8Oeff2LBwoWoVq2av5sjEgC31ekkFgsiv93F4IiIiKiSatSoEXr37o1vvvnGbfvMmTPRvHlzbN26FVu2bIHD4UBUVBRatmzptjr6//3f/+H8+fNYuXIlcnNzIQgCDh48CMD5y9K2bdtiz549bgFRixYtoNFoYDab0a5dO7frtm3bFu+88w5Wr16NOXPmwOFw4K677sKiRYtKPU1GaGgoVqxYgcmTJ2PMmDFYtmyZGG7dyRgcEZFXKpVK/Lo08xsRFcZsNkOtUqFRzRoAgJjQUFhKWc2TezOEyC3jMCL8268BOCuPkidPh+b8Wa9L0AaCvfv2AQBSUlICKjiymM2whUc6V6dThgMA7CqVOFSRiIiIAlP//v3dqn7ye+211/Daa695bH/44Yfx8MMPF3levV6PefPmFfr8m2++6bFNLpdjz549hR7TunVrvPPOO0Vet6j7iY+PR3x8vNs2nU6H1atXF3nOOw2DIyLyKlA/JFPlZzQaoVXfCia1ahWsVitsNhvkct/flgRBQHZ2NjSqIORkl828Oa4JHDN6PgBL9VurgJhq1UFwdmAvI58/7PU3q9UKu80mVm252JVK5AVocLRu3TpcuHABs2bN8nkiTyIiIqI7AYMjIvKqrFapIiooJycHOs2t1bZ0aufXeXl5CAkJ8fk8BoMBFosZDWs1wrnkk2UylExzs132IB2sMfmqd+QyqPO1ORC5JoUMBK65pwRlgdXpFEoYjIG5Ot1nn30GwLm8b0REhJ9bQ0RERBQ4GBwRkVf5V6kSBIHD1ajMZGVlITQoSHwcqnN+nZmZWaLgKC0tDQBQN64x/k46hqysrNv+wB8UFARIJJCZ3MMNqdGI4ODg2zp3eQukKhkxOJK7t0lQKGAy5PqjST6TFZwYnaqMAwcO4LvvvgPgnMMiISGB/95EREQ+KP1a3URUpeWvOHItBU9UFjLS0xEefGvC9fCbgUx6enqJzpOamgoAaFirBQDg+vXrt902mUwGrU4H+bVrkFgskGekQ3H9GqS5OYgID7/t85enwpa39QdX8CwUGHooyOVltgJeeQmkAI7K1g8//ID/HTmCPefOY+fOncjKCuzhp4HqrcWL0bdvX/Tt2xdDhw2DxWLxd5O8EgQB27dvx+7du/3dFCKiSo/BERF5lX/YT6CuJkWVU2pqKqLzrdoXFeqsMrpx40aJznP16lXIpDI0rN1SfFwWVAoFgo8dhSLlOkJ3f4eYjR9AnZOD2JiYMjl/Wcr/f/PKlSt+bIk71wfJgsERZDK3asZAFEhzRVHZslqtMMdWR1a37uJjKrlTp0/DrtHA2OAupKWmIiMjw99N8iojIwOrVq3CokWL/N2USs1ms+E///kP1q5d6++mEJEfMTgiIqIK43A4kHrjBmLyBUcqhQJh+mCkpKSU6FzJycmICo+FThuC4KAQJCcnl0kbGzdqBFtUtLiM/PWhz0Canobq1auXyfnL0tmzZ8Wvjxw+7MeWuHNVLApS92FAgkwGuz2w508ryQTtVLmYLRYIMhmEm8PTOJdf6RgMBuS1aoOcTl3Ex1R1GQwGHD58GJ9//rm/m0JEfsTgiIi8yj/vA+eAoLKSlZUFq9WKmPAwt+3RIaHi0DNfXbp0GdXCawEAqkXURFLSpTJpY8OGDaHIyYagUMAWFg6HRgtYrahdu3aZnL8sfbNrFyL0egy5vzv27t0bMB/gxOGtUve50QSplENfyW+sNhsgkwM339NYcVQ6ZpMZgkIBx805zAJpmGx+/NmlbGi1Wn83gYgCAIMjIvIq/wpN/OGLyoprQmuDyYTTly/j78vOKqFgjbrEQ80uJSUhNsoZ5lSPrIOkpKQyaWO9evUgmEyQZTvnP1GkOudOqlu3bpmcv6ykp6fju++/x2Ndu2DgPV1hMpuwc+dOfzcLwK0hdIKkwI8ZEgmHvpLfWK1WVhyVAbvdBkEqFQO4QO1HVg+WDfYjEQFcVY2ICtG0aVNMnz4dYWFhxe9M5KOcnBwAwNyPNwMAFDIZdi+chytpaTALvp/HaDQiJTXl/9k77zgp6vuNP9O2t+vHHXD0IoogFlRsQY09iahR1CgKxo5E0UQTNZZYY1Qi+BMbKhFNFIwmsSYYE2OhqIAI0rmD67d9d/rvj9mZLdd27vZu947v+/Xixe3czndn53Zn5vvM83k+qDo2IRyV1eB/G97PiZtlzJgxAABLQz1iXh8s9fVwejwoLS3t9di55E/Ll4OhKJx2xDTwooijJ07AG2+8gXPOOQcWiyXfm0cgFByCKEJ1WIwSSuI46gUUBSQMhapq4uDdjxDBg0AgEHIHOaISCIQOYVkWP/jBD/K9GYRBhh6a/OhVc+F1OUAlZh6HjByBTfuz74qmu4uqykYY/wuigP3798PpdPZqG4uLi+EpKkKwoQGxcRNgaWzAhHHjQFFU9yv3E6Io4oMPPoAgiqhvbcOyDz7Cl1u2AtBajh9//PF53b7kvmo/oSyk/Ug4sBBFCaAZUqrWSyiKBhQVSLgHabowCxiIW5pAIBByBxGOCAQCgdBv6KKBqioYVlaGlmAQW2prYbNYTF3k792r5RlVlWoZR3rJ2s6dO3HwwQf3ejsnjB2LpoZ6QFVhb2zAuKOO7PWYuWTr1q0QRBF3zP4pxlRX4aZzf4Qoz+PBFX/Gmi+/zFo4+vDDD/Hxxx+nLaNpGvPmzetVppP+t+SamkAlJpdiRSXoaFRzKhAIeUCS9FI1OvG4MEusCh2aocEE/KAT2UYcx+V5izqmUAUtwuCmsbERgUAgL6/t9XpRXl5uap177rkHmzdvxquvvtpHW2WepUuXYvny5Vi9enWfv9Y777wDjuPwwx/+sM9eY+/evVi+fDk2btyIHTt2oKampsP9/eMf/xjHHnssFi5c2Gfb0huIcEQgEAiEfkMP2Vy49Hn88fprDKfMD6YeCj7hRsqGuro6FHlLYbNq4xV7ymC12LB79+6cCEdjx47Flxs3go5EoIbDGD16dK/HzCUNDZo7q6qkBHubmkCBwvihQ1Hp86G2tjbrcT768CPs3lmHUdUTjWXfbP8Sn3zySU6Eo5K/vw1A66ZWt2AhrHt2Q+L5Ho9LIPQGknGUGxRZhvvr9ZDKtAnqYC2Njcfj+POf/2w4ZQHgtNNOO+BK+CORSFbPEwQBfAfHd4fDccC4vxobG3HFnDmmrmdyidViwfMvvGBaPDqQ+dvf/ga73d6nwtGOHTvw3//+F5MmTYKqqgM265EIRwQCgUDoN/ScoJvO/VGaU+bJlX/FqFGjsh6nrm4fKoqGGo8pikJFSbXhROotI0eOhBqNwrZrp/G4kLBarQCA6xYtBgCwDIMl86/Dlro6cI7sS/Xi8TgmjTocV/74NmPZnU9fgVgs1qvt0x0IraeeDrGiwlguDKmCq7mxV2MTCD1FkiSAprVyNZBStZ7i8frQMrwGktsFIL2ZRiHRW2Hwm2++wZ/+9Ce4nT447S7UN9dCFEUceuihOdrCgcF7771n/Lxt2zYjBzAVURRx8eyLEY6E2/2utLQMQ4ZUwm63QxRF2O12FBcX45RTTsG4ceP6dNv7m0AgAF4QcPPhh2Jo4vvRX9SGwvj9mq8RCASIcFRgHHfccTjhhBMAJB1eAxEiHBEIBAKh3ygrKwPHcZAVFQ6rFY6yMqiqir1NzTjzmGOzHqehoQHlvnShqdQ7BPv27cvJdupuG/v3W8ByHCorK3Mybq4YMWKE8fNtF5yHtz//AvMeexJelxNHHXZY1uPE43FYPNa0ZVbO3uv22roDQfZ4IFak7DuGgd1u79XYfY0kSSRUd5AiSRJUloXKEuGoNzgcdqgWq5FRpwvZhUZH7hczNDY2gqEZPH7zG6BpBn9Yfpvh9hxMqKqKPXv2dPh94Hkey158ET86Zjo27d6DBx98EH/4wx/gdrvTnhePxxGOhDFzwgWoKU06WP+6fimam/ehubkp7fkcq91cGGzCkc5Qtwtjirz53gxTfPrpp1i0aBFqa2sxcuRILFy4EIcccggArVPqSy+9hFWrVqG5uRkVFRU477zzcNFFFxnr6+VlS5cuxcMPP4wtW7aguroa8+fPx/Tp043nZTOWzrfffotHHnkE27ZtQ1VVFW644QbMmDEj7TkrV67EihUrUFdXh+LiYpxxxhmYO3duVufxa665BuvWrQMAHHXUUQCAuXPnYt68eVmN/c477+Dee+/Fs88+iyVLlmDjxo0oLi7GFVdcgXPOOcd4nZ6WzQaDQdx0000QRRFPPvlk3t2O5MqIQCAQCP0GwzAYUVOD7+vqjGXNgSD84bApV09TYxMqR45BnI/CH25FnI/C5y7F9n1f5WQ7KyoqQFEUbLt2oqK6uuCyMqqqqlBeVorxlZU4aeqhmDx6JDbt2o3fvfp6u4uqruAFAVZLupBj4ay9Fo70iSSVMRGhRBH2AnUn6DQ3NxecUEjIDbIkJUrVtMtfUqrWM6wWCyhJApXYf4WacZSaMyMIgumSuqamJhR5S0EnHGol3krsqt+Q020sBNatW4df//rXHf7ObrOhuqQEV552Kr6v24c7l72CBx54APfff39aowNddBpVdggOrk6KBB9+uwIcb8Wcc25BibcC4VgQxZ5yvPjOo+T7V0A0Nzfj4Ycfxrx58+B2u/HSSy9h/vz5+Mtf/oLi4mIsWrQIK1aswGWXXYYpU6bgiy++wOOPP45oNIorr7zSGEeSJNx999244IILcOWVV2LZsmX45S9/ibfeegteryakmRnrjjvuwOzZs1FVVYU333wTt956K15++WUjPuD111/H73//e8yaNQsLFizAd999h6VLl6K5ubnTz3QqCxcuxN133w2r1Yobb7wRAAy3lpmxf/Ob3+DHP/4xfvazn+H999/H/fffj7KyMhx99NE9/pu0tLTgxhtvhN1ux+LFi9uJtfmACEcEAoFA6FfGjR+PDWu+NB5/m+iQNn78+KzWV1UV/kAb/r3+bzh2yg/x13+/jE3bv8SPTrgcjY25uRvMcRy8xcXwt7RgSEqpVaFAURROPOkH+Mc774BjGAwrK8Oq//4PRT6fcYcwG2KxGKKxUJoAxzIWBIPBXm2fnmVFZ+Q80KIApyf/Fz+ZRKNR4+ddu3YR4WiQIiWEIySEYFmW87xFAxOGYUDJCqBqOR2F6tBrbm42fm5pacGQIUNMrd/Y2IhiT/L4X+wtxxebB1+prS6wzT/sEFS5nAgKAuKyjEVrN0AQBOysr8c/v/oG73z2OSLxONavX4+1a9fi8MMPN8bQRSCWTv8sUBQFlmFx9ORT0pZbWEuvc17Wrl2LJf/3DASBB8uyUGQZLMeB5ThcfumlaS4XQtcEg0E88MADxt906tSpOOecc7BixQrMnj0br7/+OmbPno2rr74aADB9+nREIhG8/PLLuOiii4xzviiKuPbaa3HssZqDvLq6Gueddx4+/fRTnH766fD7/abGmjNnjuHcmT59OmbNmoUXX3wR9957L2RZxnPPPYeZM2fi1ltvNZ4DAE8//TTmzJmD6urqLt/3qFGj4HQ6Ybfb066dzI59+umn4/LLLzeeV1dXh+eee67HwlF9fT2uv/56VFZW4pFHHikYp3Zh3UIlEAgEwqBnwoQJ2NPQiGBEm6xv2rUbpSUlRv5Rd/A8D1VVce5JV2B45RhccsZ83HXVM3Da3TktPSkuLgYAlJaU5GzMXHLSSSchFI3ijf/8F7saGvD2Z1/g+BNOMBVCGomE8fG6d7Cnfhte+fsT+O0zV8HCWnpd8sdxHGiGASWkl4owggBn4qKwkEjNG9i0aVMet4TQl8iiqOUbURTAMKRUrReooADoXTLV/G5MJ6Q2CjDTNECnqakZxZ5kVkyJtxyRSDhNaB4M6CV9T6zbABXA33fswe+//Bp2loWcEHcef3MVtu3bb6zT2JguoOnCUUukHrwYQ2OoFntbvwfUjhtpUhTda+Fow4YNqNu7B00NDdhfV4f6+nrU7d2LXXv34quvvurV2AcaLpcrTQh0u92YNm0aNm7ciI0bN0KSJJx88slp65x66qmIxWLYsmWLsYymaRx5ZLIL7bBhw8BxnPF5MTMWAJx44onGzwzD4LjjjsPGjRsBALt374bf7+9wLFVV8fXXX/dgT6BHY6dup/548+bNPbo5UVdXh5///OcYMWIEHnvssYIRjQDiOCIQCARCPzNxopZ/sHnPXhw1cTw27dlrLMsG/SKXY62ob6kFRVEYUTUOm7avgSRLOXMR+DweACgIe3BHjBgxAqNGjsTiv/4Ni//6NwCamGQOCqcd/VNDgIvzUbz/2V/gcXt6tW0URcHudILOyBhheR5OZ/bh3f3F559/jvIiHw4bMxpffP45rrjiirQyjGxobW1FU1Myx6OjAFlC/pBlGaqqQmW0e6YUEY56DC8IgMVq7MtC3Y87duzAiCGVaGzzY8eOHTjiiCNMrd/c1IzhY5MuhCK3dnOjsbHRKLsZDEiSBIai8OiJx6Da7cTVUyYhKkpY8tVGSCwLr9cLi8UCSZbBsSyKS0rauXn08+7ra55ApXcE3t/0Cr6rX4sRJQd1WJJGUVSvBUdVVQG3By3HnQDZ4wEdi0H2eFD+93d6Ne6BiM/na7esuLgYe/fuRSgUAgCUZNxE0x+nOpStVmu70lWWZY3OhGbGYlkWHk/6tUhxcTFaWlrSnpvNWGYxO3Zm9lBRUREkSYLf7283Rnds2rQJwWAQv/jFLwquYyURjggEAoHQr1RVVcHlcmHznj2YNm4Mtu6txaU/mJn1+voF6msfLAEAsAyHpb/5AF9t/a/x+1y0/nUlBI5CutuTyb333We4g+x2u1H3nw2qqkKSRJSXVMNmdaDSqjmBLKwFstD7VrE2mw1sSwsoQQATCYMSBNDxGFyu/u000x2SJOGTf/8bp06ZjGljx+DdL1/A9u3bTQs/t932S9TWJrv6zZ49GwsWLMj15hJ6iC5u6PlGxHHUc6LRKBSXB6pFyzKLRCIFKaRs/vZbTBo+DCUuFzZv/tbUuqqqorWtxRCLAMDnKQOgZR8V4vvtKYqigKFpI8zZ7tK+I0U2K3xjx+O3v/1tt2Po5+XZR96Cob7RmDXtevBiDG+sewos2z4Di0LvnWqKogAci9hBk9J/wTADtt15vvD7/e2Wtba2oqSkxBBvWltb07q16QJOprjTFWbGkiQJwWAwbZm+TZljpdKT7epqO7MZu62tLe39tLW1gWXZDgW57jj11FPBMAx+85vf4Pe//71pwbsvIaVqBAKBQOhXKIrC2DFjsLVuH2qbmsGLoqlJuh5UfeGp1+Kuq57Br+dqLekPm3Bc2u97i37XrFA7BgHa3beDDz4YBx98sCnRCEg6MDgm8+4gB1Ho/YRalWU4N28C19gA30cfoOLlF4FYrOAcXF9++SX8gQBOO3wajhg/DkVuNz744ANTY6iqin376nDCtLNxzzXPo8RbkbMOf4TcYLgedFGZYUg4bw8JBgJQHA4oibLT1BDqQsHv92PX7t2YMnoUDh01Ehs3bDTlRg2HwxBFET530i3gc2nly/rkcTDRkYRjRtfR9+0Q3whYOTvK3UMxrHgsaIru0L2Zu+JGc85QQseEw2GsWbMm7fHatWsxadIkTJo0CSzL4qOPPkpb58MPP4TdbseECROyfh2zY61evdr4WZZlfPLJJ5g0SRMKa2pqUFRUhA8//LDdWBRFYcqUKVltU6ojSsfs2KnbqT+eMGFCj29i/uIXv8AZZ5yBW265BevXr+/RGH0BcRwRCAQCod8ZMXIkvvjPJ9hRXw8Apjqq6YJOkacMI6qSrXxtFjtomgaTo7uN+gk/F+6lQkSfNDOZwhHDQRCFjlYxxbBhw7DPZodYXgH/zFNAxeOoWP5Swd2p/+D99zF+2FCMqa4CAJx2+GF455//xNy5c7PuFhUMBqEoCg4efQSGVYzCmGEHo6V58IXoDmQMxxGrC0cscRz1gHg8jkgoBNnlhuTSRODUEOpCQc+4mTZ2DOpL/Xj+vQ+wdevWrMuidQeGx5ksQbFZHbBabO1cCAMdhmEgyTK2tWkCIEUBo31eSKoCa5bnP/2cS2V4EiiK7lAlUlU1Nzd5OtCN1ByUwR1oeDwe3Hfffbjqqqvgcrnw0ksvAQAuvPBC+Hw+XHDBBVi+fDk4jsPkyZOxZs0arFy5EvPmzTPlyjYzFsdxeOGFFyAIAqqqqvDGG2+gsbERl112GQDtc3vllVfi0UcfRVFREWbMmIEtW7Zg6dKlOOuss1BVVZXVNo0YMQJ///vf8cknn6C0tBSlpaUoKyszNfY//vEPWK1WTJgwAe+//z6++uorPPbYY8bv4/E4/vtfzRW/f/9+RCIRQzw77LDD2pW6AcBtt90GQRDwi1/8AosWLcLBBx+c9X7uK4hwRCAQCIR+Z+jQoVjZ1Ix3PvsCHrfblKXYlmjnHhfSA0rjQgx2u8N0Nk1n5GqcQkWfNHNse+EoF06M8rIycI1NUC0WSJZi0JEwAORVONq9ezeWLVsGWZZht9sxb948rFmzBj8/8zTjOadOOwyv/utjfP3112lhoV2hTyR1d4LPXYK9uzZ3tQqhn8ksVVNJqVqPqE+I/ZLPB9VmA2WzFaS7bs2aNRhdNQSlXi98LhfcDgfWrl2btXCku6hcjvTjldvp7bCsZyDDcRxUAAv+pU1sWZrGyh+fhqgow5WleG4IRxnnTRo0FLS/kaNC6bNzbCGcuWtD4QH1mqWlpbjuuuuwaNEi1NXVYdSoUXjiiSeMsrAbbrgBHo8Hb731FpYtW4bKykrMnz8fF110kenXynYslmVx77334pFHHsH27dtRVVWFBx98EGPHjjWec/7554NhGKxYsQIrV65EcXExLrnkEsydOzfr7bn00ktRW1uL3/72twiFQpg7dy7mzZtnaux7770XixcvxvPPP4+ioiL86le/MjrLAdo1wu233562jv548eLFmDZtWrsxKYrCHXfcAZ7ncdNNN+Gpp57KuvtwX0GEIwKBQCD0O7rDaM3W73Ho5Mmm1mUYBk6nC+FoejhhOBbsVU17JvqF8GC9c6mXFjAZ7ZMZhoWUgwm1z+cDk9J9SP85n8LR+vXr8b///Q9D3U7UhiKYOHEiRElCha8IUZ5HSzAISZZR4vFgw4YNHQpHoVAIDz34MGKxKBiGwVU/v8qYZOqlLD53yaBzJQx0ksKR5qBQWSIc9YQ9e/YAAKTiEoCiIBaXYPfu3XneqnRUVcVX69fjlMnaHXqWYXDY2NFYv24dLrnkkqzGCIe1SbjLnl5a67C5exW6W4joAbwPHT8dFoYxuqCpVPal2vp5kqIyHEd0x93TFEXOWVl5IeH1emG1WPD7NT3v6NUbrBaL6XPsnXfeafw8Y8aMDp9D0zTmzJmDOXPmdDrOvHnzMG/evHbLM8u4zI61bNmyrjYf5557Ls4999wun9MV5eXl+MMf/tCrsYcNG4YlS5Z0+vuqqip8/vnn3Y6zatWqtMcMw+D+++/vdr3+gghHBAKBQOh3Jk6caFiQS0tLu18hA5/Xi2CkLW1ZMNyKkuLiXG2icbGbqy5thYbuKqLp9FIEhmZy8p59Ph+oaMR4TCeEo56EReYKnufhtllx1zFHYN57q7F/v9Ze+jfLXsZN5/4Y73z2Obbt249DR43s1EWxZ88erF23BjXFE7C79Vts3LgRjkTWi9dwHJUizscRiUQ6HIPQ/xgikS4cMUy7XAtC9+zatQuUy2XkG8VLS7Ft5848b1U6+/fvR0trK6aOSea+HTZmNJ5c9Tbi8bjhWu2KaOJ41di2D1aLHf5wK+J8FDaLfdB9r/XyoCEuJ4psSaGIV5Ss9hWQIhwh03FEQVHan08URQHL5mAaWmD3dcrLy/H8Cy/kLffL6/WmhTQTCLmECEcEAoFAyAuVlZU9Xre4uBiNrXXYtW8rKkuGwh9uRZN/PyprzLU97QpdPBms3Vn098UyGY4jmoGcg/fs8/mgCgIoQYBqsYBOTLY6quXvL3ieh5VhjNyOVMfJ42+uMn6WFaXTv7u+zs+O/hV+/+F1EEURLS0tcDu94Fjtzr3eiWmwdV8ayOgike44UkipWo/Ytn074qVlxmOxrBx1mzZCFMWsM8H6mq1btwIALBxnOAmdVhtkWcbOnTuzKlfjeR4A8Lvnb8Cv5jyJv/77ZWza/iUOHn0E4vF4n25/f6ML3zFJQhGSwlFUlIzf9RSKZjoUjmRFyo1w1HGAUl5LzcvLy4l4UyDoTUA6IzefwQMHsrcIBAKB0Kc8/NBDWP3xxzj6qKPwm7vuysmYZeVl+GrNBvz2mauMi/p9jbsw5ajTul85S3ThaLB2XtLfX2ZpAZ0jx5EuENHRKGSLBUwkDIvNlvUd7L5AFEVYGAZcokTC6XQCAG788dkYO3QogpEIyot8uHPZKxhdVtbpGICWBcXSWh5Uc3NzWtvuIo/2c0NDAxGOCoSkcKRd+iokHLtHbNuxA8LIpJNHLCuHIsuora011eSgL6mrq4PNYsGCJc/gj9dfg2UffIQvt2hiUm1tbVbCkSzLoCkav5n3NCpLhuKSM+YjzkexavWLg+6coItD4YxumhFBNI6RPYWmGMiKNnlPFXNESex14wmKogBZBtdQD6moGEwkDEoQAHVw3uwhmGfWrFmGs7gjsikf64qzzjoLZ511Vq/GGEgQ4YhAIBAIfcrmTZugqio2btyYszHLysqgqgruuuoZ7aL+9Btx9zPzUFFRkbPXGOyOI/0uHJ0ZZkrRUHIoHDGRMORE3pHH6+v1uL1BEARwNA2O0YQj/a7w+2vXo9GvlRb4N0awv6XVaPmbidGNjmbBJMQHTThKCk26iNTY2Ihx48Z1OA6hf0l2VdOFI1KqZpZoNIq25maIR0w3lokJ99Hu3bsLRjhqbW1Fmc+Ly06ZCRUqfnzMdFx15mm4afEzaGtr634AICF0aMeJ+pZaUBSldfFU1UGXe+d2azlO4RQhVVZVRAUBLperV2PTiS5rsiyBTWnEIElCrx1qNE0DcR4VL7+IxgsvhuezT2HbtRNqaemgzE8imOfRRx8lNwhyCBGOCAQCgdCnhCMRlDvsaIlG29117Cnl5eXwh9swtGIUWIaFS5bAC3EMGTIkB1usoU8OBqtwpL+vzIwjiqKg5GBipGcZ0YmcIzoSQXGRr9fj9gZJkjThKDGp4DgOZ5x+Or7++mt8snWb8byDJk7stKNaUjjSHEeiKKK5qRnDi5NCE8dZ4XZ60dTU1IfvhmCGpHCkl6qxiBPhyBR67peUkiWn2mygnE7U1dXla7PaIQgC4oKA+5avAABwDIOPHnkAFpbNWiykaRqyIuG3z1wFQHMYLv3NB+DFGLxM7powFAJ6U4lgyr4JCyLUlN91h35eVzNKxyhaWy5KQppwJMqiEcrdUyiKAiwcGi64EFJRMfwzTwElCKj4+9uDvisqITvGjBmT700YVBDhiEAgEAh9hiRJCEejGFdeisZoDJFIpNd3MAGgoqICqqqgLdiEsqIhaPZrLaJzKRzpDNYL0E4FMYqCmgOrv8fjAUXTRjc1NhpBaVXu/z5mEEURDEWBTvyTJAk33HijqTGSwhEDmqIhyzKamppQU0IhzkeNEF2fuwSNjY198TYIPcAoVUtMXlUTIgJBo7m5GQBAxePgGrRjrlhRCdnlNn5XCFgsFrjtDvzuissAJAObRVnOWqzQn/fruUvA0EzaeaC3gkehYbPZwLEsgnzSmaGLSNkKR4bDJ+OmA41EnpwkwI5k2Zso8ll3bOsMiqJAURTECi0vUbIkBE0VxHFEIPQBRDgiEAgEQp+hty0e7nVjXWMz/H5/zoQjAGjx16OsaAhaAg0AtJanueKnP/0pxo4dix/84Ac5GzOfhEIhbNuWdNXoAkimMEaByrz27xE0TcPpdsOfCMXmYrG8dlQDtPJDNnEHnKHpHmWVGMIRxYKhNfEhFA7hX2v+iumHnJwSontkQU2mD3Qyw7FVlkU8Hs3nJg049ON52RuvA9D2Zd2ChRA5rqBa1Hu9XvjDYQDAsLIytASD2LRrN8ImjkF6p7GK4mq4HEnxRJDivQ6MLjQoioLX40GQTwqp+s/ZZrTpQo2ippc504lyP0Hi05YLYu9L1RiGaSdUAQBUlQhHBEIfQIQjAoFAIPQZra2tAIDRXo/xeOjQob0etywRXNycEIya/fWwWKzw+Xzw+/29Hh8Axo8fj/Hjx+dkrN6we/duvPfee2m5GjRN45xzzjGV6fT888/j3XffNR7b7R1PfnLpsPJ6vahPOI6YaDTvQdGyLINJvD+WpnsUAq47tRiaBU2xiMViAICLfngdhleOMUJ0P/jsL2htrc/dxhN6hd4lC3oXHZYBTxxHptDFt5Yzzobs8YCOxcA11EPhOIQLqEX9kCFD0BoKYd5jT7YLx862m6d+gyMaD6UJR9F4OGsXzkDC5/PCzyfFnUAPhaP9gd0ANCfQ0KIxECStA50gJsdWFAWiJPS6UQJN00AHzllKVXodvE0gENpDhCMCgUAg9Bm642JcsQ8A0NLSkpNxrVYrvB4vWgNaKVBroBFlpWWDsqzsww8/xNtvvYUh7qRTqy4YQlFREc4777ysxwmFQhhddjAuPupWrNvzMd755rlOn5ur3Vjk84GORbVA2Wgk78KRJEmGcMTQVI+EI0mSQFE0KIoCQzOGIDGyegJsVgcqrZog53J4Ubdva+42ntArBEHQRCM9i4XlIPB8N2sRUtEn4yV/fxuNF14M19frYdu1E7GRo7IOne4PRowYAQD41YUXYEx1FW4690f4+OsNeObv7xq/6w79WBWM+FFeXG0sD0b8eXdO9gVeXxGC9cmcqgDPg6ZpIzi7O/TPxvLPH9Ye0xweu+DvqAvsAADwQsx4rphwH/VWOOrUcaQQxxGB0BcQ4YhAIBAIfUZjYyNYmkal0wGnxZLTzJfSsjK0BrXw4bZgE8rKO26fPtDheR5DvW4sOulYY9nP/vGvpIMiSwRegMPiQYlrCHyOZOv4zA5BuewYVOzzgd29B5QoArKcd+FIURTo96EZqmeOI1mWwSQCxWkq2ZnLarGnPc9qsRluJEL+EQQBVEppjMqypNuOSfTW7I0XzIZYXmGEEZf88wPUFEhHNQCoqakByzCICQIcViscZWUIRqMoLyvL2i2kd4X8fs9GDC0fCX+4FeGIH9FYGKWlpd2sPfDwFRVh355dxmM/L8DrdmUtwOjC0YVH/AJDi8YYN3GGFY1Ba6Q+zXHEC5oLKSeOo46O4cRxRCD0CUQ4IhAIBEKf0dDQgDKnAzRFocLpQH19z0p3gsEg3n77bYwdOxZHHnkkAKC0tAT+1oRwFG7CiHH5DV7uKwRBgDWj85mlB63EeV4Ax2gTP47pPJRUVRWjDXVvcblcYONx0AkBJdu7132FJElg6N45jlKFI4ZijTEaWvZCURRQFIWaIWMR52NEmCggeJ5PlqlBE47Miq8HOnqJcPnrfwJSBQVFQfWMGXnaqvZYLBaMHDkS3+3dayz7bm8txo4bl/UYHo8HFEXj9Q+WYPTQiUZ2GYBBKRx5vV58J6SEY/MCPJ7shX7DjeaqxLDiscZyK6c5MOMpjiP955w4jjpq8iDLYFkyxSUQcg35VhEIBAKhz9i3bx+GODQnxhCHDfsT7ZzNsmbNGrzyyiuorqo2hKPi4mJs3rsdABAIt6Kk5ODcbHSBwfM8LEy6kGNhaPOOI0GAPSEYcUyyK1A7xxEAms5NrZrb7QYdj4OOaxOFXASj9wZFlsElRDGa6rlwRFMJxxHNGJlHi//8WwDJtt3f7lxr3Fkn5B+e542OagCgchxEknFkivHjx+OWW25p56SjKArHHHNMnraqY8aOG4dv160FAMiKgi21dbjw+BOyXp+maZSWlGJs1RQju+y7neuw7J3HTGXLDRS8Xi8CqRlHggBvUfYCmS7UyErHDQdSS9X4HAlHLMtC7Ug4UvLrOGpsbEQgEMjLa3u9XpSXl5ta55577sHmzZvx6quv9tFWmWfp0qVYvnw5Vq9ene9NIaRAhCMCgUAg9Bn7amsx2andcaxyOrG6rq6bNTpGn5ynahw+nw+BiBa+HQy3GaUFgw1BEGDJCB2yMoxpNwvP8+BYTTCypDiOFDX9wltR5Jw6jqh4DHRiQlIIjiOH7jjqVamadvlEg4Gqan+HK350G4ZVjDZKNEZVT8T2BiJMFAqCIEBNcSEoLAdJEKAoCslDyRKapjFz5sx8b0ZWjB07Fu/+4x+ICwIa/X7EeB5jx47tfsUUKodUQpJFI7ts+95N2vLKykFXhur1ehHmBciKAoamERBE+EycUzsVjlTteJjuONIaJvS2Ox3DMFqpmqqmB/PlUThqbGzElVdcgXie3Iw2qxXPPf+8afGIQMgGIhwRCAQCoU+QZRn76utx2iStPKDK7UTT1u2Ix+Om7zR2lLvj8/kQCgfACzHwQjzv+Tl9hSAI4DImthxN9chxxFnbO44UJV08UVRt4pALnE4nVEEAxWvOm3y3sU4Nx2ZpCpLU8d3xbsfQS9VoFjSt7b9YPIzKkqHwh1uxa99WKIqM4kEqZg5EeJ6HkpFxBACiKMJq7bx0kzAwGTVqFBRVxY799ahPBHePGjXK1Bjl5WXYvTVZXt0SaIDX44PNNvjyy3RRPySI8NmsCIsShpvoHqcLR1KGcCSrEmiaMcQiICki2e3puXBm4fTvc4ZwpMpy8nf9TCAQQJzn8euLL0RNRf+KN7sbGnHf8hUIBAJEOBogqKoKURRhsVi6f3IBQIQjAoFAIPQJDQ0NkCQJQ11aro7+f11dHUaPHt3r8T0eDyRZRLO/AUD2bYMHGgLPw5FZqkbTph1HAi+A8+nCUXKivL95D5x2j5HNE44EAOSmVM1utwOqCibRqjvvwpEoGiIc14N9COjCkXb5xNAsKGiuolffewojqsYbWSiTRh+OsmGDLwtloMLzPBQ26UJQOdZYToSjwcfw4cMBaJPp/a2t8Hm9ps8R5eXlWPflV8bjlkADygdpEwZDOBI14SgoCKYcorpQI8npx1RZEcAyLOJ8inCU+Lm3wpEuVlGSBFWfeKsqIMt5D8euqSjH+KFD87oNZvn000+xaNEi1NbWYuTIkVi4cCEOOeQQAJrr+6WXXsKqVavQ3NyMiooKnHfeebjooouM9fXysqVLl+Lhhx/Gli1bUF1djfnz52P69OnG87IZS+fbb7/FI488gm3btqGqqgo33HADZmTkqa1cuRIrVqxAXV0diouLccYZZ2Du3LlZ51x9/fXXWLx4Mb7//nuoqoohQ4bg4osvxplnngkAuOaaa2C323HyySfj2WefRXNzMyZNmoRf/vKXqKmpMcZZvnw5PvjgA+zZswcWiwUHHXQQbrrpJuNYBCTLAq+//nosXrwYu3btwj333IMTTjgBixcvxkcffYSWlhZ4PB5MnDgRv/3tb40S/1AohCVLlmD16tUIBoMYNWoUrr322rR929cQ4YhAIBAIfUJtbS0AoDrRRr7a7TSWmxWOdMdRqvNIv6htaK1NezzYEMWOHUemw7EF3nAapQpHy//xJIBkNs/G7V9CFHNTYqU7y5hIBBRN5+0usE4sFkNUlRCTJNAU1WvhiKVZSJKMIl8xDh55lJGFEuej+L8378XBh/deICXkBp7nITOpjiPOWJ4vHnrwQezcobUrp2kac6+6CocddljetmcwYbPZUFpSgtrmZuxvbUV1dbXpMcrLy+EPtUKSJbAMqwlHlYMv3whInj8jiYDsiCCayqQzhCMl/dwhySJohkZDax3ifBT+cCv2Nmif+d7eSDCEgdSco8TP+T7XDDSam5vx8MMPY968eXC73XjppZcwf/58/OUvf0FxcTEWLVqEFStW4LLLLsOUKVPwxRdf4PHHH0c0GsWVV15pjCNJEu6++25ccMEFuPLKK7Fs2TL88pe/xFtvvWUIt2bGuuOOOzB79mxUVVXhzTffxK233oqXX37ZuIZ8/fXX8fvf/x6zZs3CggUL8N1332Hp0qVobm7Gr3/9627fdzgcxi9+8QsceuihuPfee2GxWLBz506EQqG0523ZsgV1dXW47rrrAABPP/005s+fj9dff91wCzU2NuL8889HZWUlIpEI3nzzTcydOxd//vOf00Tr5uZmPPbYY7jiiitQUVGB8vJyvPjii1i5ciWuu+46jBo1Cn6/H59//rlxnSeKIm644Qa0trbi6quvRnl5Of7xj3/gF7/4BV566SWMGTOmJ3920xDhiEAgEAh9Ql1dHSwMg1K7Jh64LRa4bVbU9SDnSBeMqBQnjH5R29y2P+3xYEMUBHBMpnBEmxaOBEEAx7YvVfvpKddgwsipRjbP+JpD8dX2YC+3WkMXjuhYDBar1XiNfNHm92NPOIyzR4+Aqiqm9yGgXcyytDYpYWgOcUlEVXUVBFEwslAURUGLvx5DB9gd58FMLM5D4VK6qnH5FY6CwSBWf/wxhntcmFJWir9u34VPPvmECEc5pLKyEvtbW1Hf5kflKPMibllZGVRVhT/YhNKiIWgLNWHc5MH593E6tRs7YVGErCiIiaKxLBsYhgFF0ZCUdDFeUkQosoxPv34PJxx2puHItFgsYBimRzlzOro4RMky9FtKVGI80lXNHMFgEA888AAOP/xwAMDUqVNxzjnnYMWKFZg9ezZef/11zJ49G1dffTUAYPr06YhEInj55Zdx0UUXGSKgKIq49tprceyxxwIAqqurcd555+HTTz/F6aefDr/fb2qsOXPm4JxzzjGeN2vWLLz44ou49957IcsynnvuOcycORO33nqr8RxAE3bmzJnTrWC8d+9ehMNhXHvttYb4csQRR7R7XmtrK5YsWWK4h8aOHYuf/vSn+Nvf/oaf/OQnAIAFCxYYz5dlGUceeSROP/10/POf/zSeo+/rxx9/HJMmTTKWffvttzjyyCNx3nnnGct+8IMfGD+/++672Lp1K1555RWj5Hb69OnYu3cvnn/+efzud7/r8n3mCpIGSCAQCIQ+Yf/+/ah0O0GniAVVTif279+fk/H1i9qWQGPa48GGKIjg6HTbPUfTpjtCCSIPjk44jtik46jEV4ERVeNQM0QLjrVa7Tkr3dHHoeMxcAVQw89xLE6pGYpRPg/cnKVHooEgCGATzhWW5iAIIqqqhqCxrdZ4TluwCaIkYtiwYTnbdkLviPHx9K5qiYllPJ6fznfbtm0DANx+1GGYd+hBOLlmKL7fsiUv2zLQ+eqrr/Dqq6/i1VdfxcqVK43sstKyMjQFgmj0B1Baar5stKSkBADQFmrW/g82G8sGG/r5MypKiCb2n9lzqoXj2pWqSYoIu92OQ8YcaTgyTzr8HDgdvT9fpwpHOvrPxHFkDpfLZYhGgOZAmzZtGjZu3IiNGzdCkiScfPLJaeuceuqpiMVi2JJy3KJp2uh8CwDDhg0Dx3FobNSu08yMBQAnnnii8TPDMDjuuOOwceNGAMDu3bvh9/s7HEtVVXz99dfdvu/q6mo4nU489NBD+PDDD9GWyEPLZNSoUWklZ8OHD8eoUaOMbQGADRs24IYbbsApp5yCY445BieccAKi0Sj27NmTNpbP50sTjQCtY+X//vc/LF26FN9++63REEbn888/x5gxYzB8+HBIkmT8O/zww/Htt992+z5zBZFjCQQCgdAn7NmzBy6GQUyS0BbnERUllNssqN271/RY+kk0tQOYfleqLdiU9niwIYoiOHu66GI2n0eWZSiKYggeqY4jOSMcW5alnN2tNS7seb4gLuRFQcQQlxN2loWTYxDvQcCtKIpgdQGOsUCMixg6dCj++8mnUFUVFEUZ5ZNEOCoc4vE4VHcy7DffpWrbtm2DneMwJJH9NsbnweqNW7QuigUgsg4knl68GLtTzisTJkzAxIkTUVxcjO83bURrMNgjwUdfxx9qAS/EEItHUFxcnLPtLiRsNhsoikJMkhAVNeHI7DmV4yztS9UUARarBTTNGI5Mm8WRkxs9SeEoJZA7IXoVwvlmIOHz+dotKy4uxt69e42yrczvkP44GEw6lK1Wa7t9z7Ks4e41MxbLsvBkBLQXFxejpaUl7bnZjNUZHo8HixYtwtKlS3H33XdDlmVMmTIFN998c1r5V0ff+9Rtqa+vx4033oiJEyfil7/8JcrKysBxHBYsWNDO2dxRB+A5c+aApmn87W9/w7PPPouioiKcd955uPLKK0FRFAKBALZs2WI4uVLpzzwvIhwRCAQCoU/YvWsX/IEAdviDeO27bVjf2IyfjB2Jr3pQqqbb2VPvwujBmv5QM1iG7Xay5ff7sWbNGtA0jaOPPrrXwZz9hShJYOn0Ei+OoU3lEOmTY0si20gXPgBNKEpFVuScC0e0IOS9dEBVVcTicTgSAcl2loU/Gu1mrfbwfIpzi7GAF3gMGTIE0XgE4WgAbqcPDS21oGkaVVVV7bISCPmB53moRSmOo5Rw7Hywfft2jPJ5DEfmKJ8Xkixjz549/ZZXMViIxWI4f/xonDZyOK5891+IJr7XRUVFqGvS3EIdTYy7w+VygWVYBCNtCITbjDEHIzRNw2axICZJiEva+dbsOdJisUCU0s9LoiLAYrcgzkeMZVE+kpPSckOgkIjjqLf4/f52y1pbW1FSUmKIN62trWnd2nTRJFPc6QozY0mShGAwmLZM36bMsVIxu12TJk3C448/jng8jrVr1+LJJ5/ErbfeijfffDPtdTNpbW3FhAkTAAD/+9//EIvF8NBDDxl5Yfr2Z9JRyb7FYsG8efMwb9487N27F2+//TaWLl2KqqoqnHHGGfB4PBgzZkxWuU19CSlVIxAIBEKfoCoyThsxDKN8Hlw9ZRL+cNKxKLL2rDxIF47klPbpen5OINxq/NwVb7/9Nn7/+9/jkUcewSeffGJ6G/KFJEntwrFZmoYkZZ8Nod/x0kOxGZoBTWljtuuCk0PHkdH1RhTAsvm9kBdFEZIsw56YUDg4FtFIpJu12iMIAjhG+7xxrA2CIKCqqgoA0Ni2z/i/vKwi72IZIQnP80auEZB/x9GuHTtQ4066Lmo82mRj9+7dedmegUw8HoedZWBPiMKxhJMwdeJoZnKrQ1EUPB4PQhE/QlE/gJ4JUAMFm82KmCQjnjjfZnNeTcVisUDMcByJsgCr1YqYkN5VzZFTx1GqcCQZ20LInnA4jDVr1qQ9Xrt2LSZNmoRJkyaBZVl89NFHaet8+OGHsNvthniSDWbHWr16tfGzLMv45JNPjDKvmpoaFBUV4cMPP2w3FkVRmDJlStbbBWif92OPPRazZs3Cvn370s4NO3bsSCs527NnD3bs2GFsC8/zoCgq7Zz/0Ucf9SjDa9iwYbj22mvh8Xiwa9cuAFru0r59+1BaWoqJEye2+9dfFMQVzc6dO3Hfffdh7dq1sNvtOPPMM3HLLbd0e8CKRqNYvHgx3n33XTQ1NaGiogJnn302fv7zn5MDBoFAIOQRVVURicZQM2o47CwLu0s73ewOhiCIolZ+ZeKOoF6WJaYIRxRFwWa1IRjxw+Xu3lIfi8Xg9KmIhShjYjEQ0MKYOxKOss9mSQpHyXOjhbNBkOLtHEeSbO5v0xW6hZqSZDD2/LZHDofDAABXwmni5DhE2trfReyOWCwOC6N1SLEwVsTjcVRUaJ2Wmtv2Y/TQg9Dsr0flkMocbTkhFwg8b+QaAfkNx5ZlGfvq63H6pHHGMgfHosThMLpRErKHF3hYGRbWxPFG/5secsghOOqoo8AyjOlOnjoejwfhWBDhaNB4PFixWqwQZBl8YrJrNuvOYrFAlNO/T6LMw2p1IxwIGMvifATe4t4LR/pcL7VUjSKlaj3C4/Hgvvvuw1VXXQWXy4WXXnoJAHDhhRfC5/PhggsuwPLly8FxHCZPnow1a9Zg5cqVmDdvnilnmpmxOI7DCy+8YNyceeONN9DY2IjLLrsMgHZ9ceWVV+LRRx9FUVERZsyYgS1btmDp0qU466yzjBs6XfGf//wHb7/9Nk444QRUVlaipaUFr7/+OiZPnpz2+S8uLsbChQtx1VVXAQD+7//+D2VlZTjzzDMBwMiHuvfee/GTn/wEO3fuxPLly7Pu9rtw4UJMmDAB48ePh81mw3/+8x8Eg0Fj3DPOOAMrV67ENddcg4svvhjDhw9HOBzGli1bIIqi0e2tr8m7cBQMBnHZZZehqqoKTz75JFpbW/HAAw/A7/fj0Ucf7XLdu+++Gx9++CEWLFiAsWPH4ptvvsGTTz6JQCCQdysXgUAgHMhIkgRJlhEUBGxrC4CigNE+L+KJi7p4PN4z4Sgj18dqsyEQ8KPE1n3uBM/zYDgVLGe+I1k+kWS5fakaRUMSpU7WaI/+ftkU4YhjOEiK2D7MVBbBcbm5PKATghelyKCZ/Jqc9ZIxl0X73Lk4DqGEmGSGeDQGH6uJQlbWjjgfg8PhgM1mR2sib6st1ITRE0lHtUJCEIQMx1H+StVaW1shSRIqnemCd4XDhvr6+n7fnoGMqqrgBRFWhgZH06CQ/JsOGTIEd999d6/Gd7lciMRCiMa140e2E8FseeuttxAIBHDRRRflXeywWq0QZAW81AvhKKNUTZIF2Gw2xOPJphgxIYoqZ+9L/gzhKPWGUg9Fr1yzu6FxQL1maWkprrvuOixatAh1dXUYNWoUnnjiCaMs7IYbboDH48Fbb72FZcuWobKyEvPnz8dFF11k+rWyHYtlWdx777145JFHsH37dlRVVeHBBx/E2LFjjeecf/75YBgGK1aswMqVK1FcXIxLLrkEc+fOzWpbhg0bBoqi8PTTT6O1tRU+nw9HHnlkOyFm/PjxOOmkk7Bo0SK0tLRg0qRJuO2224zP2ZgxY/Cb3/wGzz77LG6++WaMHTsWDzzwAG6//fastmPy5Mn46KOP8Kc//QmyLGP48OG45557jKBxi8WCp556Cs8++yxefPFFNDc3w+fzYdy4cZg1a1ZWr5EL8i4crVixAsFgEKtWrTKCpxiGwS233IJrrrmm0zsEkiTh3Xffxdy5c3HppZcC0NrS7du3D3//+9+JcEQgEAh5RLfnvrp5G17dvA0sTWPlj0/Df+rq036fLXrnozgfNwKIgeTFoS2Li0RRFEEzChiWyVt5Sk/oyHHE0BQkE/sws1RN/1mQ4xDbCUcSOHtuJjBGLb+spHXXywe6cOROTDY8Fg68IJgOI47GYqh0aHdFrawNiqJAkiSUFBcjENYcTP5QM0pKDs3xOyD0FFVVIfI86GgUlCCAiYRBCQJA03kTjgCt9Xlq84AiiwWNDQ39vj0DGf1mAscwoCgKHMPk9MaAy+1CrCWMWDwClmFzKkjIsoynn34aADBjxgyjzXa+4CwW8LIIIdEwwWz1hs1mhRhO/z4JEg+73Y5YSsZRXIjkJBy7Q+Eoz44jr9cLm9WK+5avyMvr26xWeL1eU+vceeedxs8zZszo8Dk0TWPOnDmYM2dOp+PoGT2ZpJab9WSsZcuWdbX5OPfcc3Huued2+ZzOqKmpwYMPPpjVc88++2ycffbZnf7+jDPOwBlnnJG2bNWqVWmPU/d1KpdeeqmhZ3SGy+XCTTfdhJtuuimr7e0L8i4c/fvf/8bRRx+dllb+wx/+ELfffjs+/vjjToUjVVUhy3I75d/j8UBV1T7dZgKBQCB0je40mT1xLI6oLIeuGRw/dAg2Nrd2GA7YFXppmaoq4HneKGW2cNqFYzYX8zzPg2Jk0Aw7oIQjWVHAZOwvhqLalZh1RUelaixjAU3T7UvVJAEOa27KvY2/s6oan4l8EUiUSnh04SjxHgOBAMrKyrIeJxaLwerRnCI2LtHCOhqFr6gIgXArVFVFMOzvNgtl7969+Pzzz2Gz2fDDH/4w726DwYwuLng+/x/iI0fB89mnsO3aCViteXEfRhLZWr//8mvcMPUQvL97L7a0+nFcdSX279/fzdqEVAzhKOHK5BgGkpT9sbE7HA4HWvc3I8pHYHc4TJ+7uiJ1vpLZfjsv0BRao3FE9H1q8phksVohBpLfJ1VVIUgCHA4HREmEJIlgWU7LOMpBF9SuhKN8RZaUl5fjueefN843/Y3X600LnSYQcknehaPt27e3s1hZLBYMHz4c27dv73Q9juNw7rnn4uWXX8Zhhx2GMWPGYMOGDXj99ddxySWX9PVmEwgEAqELOI4DRVEoslkxpih598ueKIEye9c2khJiHIlEDOHooIMmYt/+OozPIpyR5+OgGRk0qw6YUjVVVaEoClrifJozgaYoiD0qVUtOBDjGAoqmOygtEMFxvb+oB5LCEVUAN3QCgQBoioI7UaqmC0hmhaNoNAobpwtHDmOZ1+tBsDEAQYxDlIRus1Bee+01IyB0+PDhmDx5sun3RMgO/fPvP+5EiOUV8M88BZQgoOLNP+dFRE4VNhat3wCGovCHk47FI1+shwQKTU1NRkh2TU2Nqc/ngYbuXmUSYf8MTeVUOLLb7YgLUcT5aM47cRaEWJRCMBjCtsYmI6jdrHBktVrgl5PZe7IiQVUVQySKC1G4WC9ifDQnXdU6Fo7ExLbkr1StvLyciDcFgizLXRpKSAMLc+R9b2W22dPxeDzdqrV333037rrrLlxwwQXGsksvvRTXX399r7ZJD/MsRPRtK+RtHAiQ/ZgbyH7MDYN1PzrtdoSF9DKoiCCBYWg4TN65DYVCcFl9CPN+RCIR46LsloW34JaFtwDofj/G+ThoRgXNKBBFcUDsb30C9Np32zC1vBSvfbcN6xubcdmk8eAFIev3oI/D0ikZR7QFNEV3mHFksVhysn9Sx6AoKq/7PBAIwGOzGiVzPps2sQiFQllvl6IoiMWjsCecRrrjKBaLwe12o37PbkRiWkmcXi7Q2djRaBSuUgHhZgvi8fiA+Dzmg1wcH/XPv1RaCtVigWTRXO4UZ4EkSf2+7/XXu+vow+GzWY0MuAnFPnyvUHj0kUfwzYYNAIBDDj4Yv3/ssazHFgQBL7/8crsGABRF4ZxzzsGIESNy9j4KCd2VydA0VFXN2d/UbreDF2LgxTjsdntOz9eFdHwEgMrKSlQoEooSoov5UjUbRCXZcEBIBGXrIlGMj8Ll8BqOo96+X5vNBlBUh46j1L8V4cBl1qxZXbo4P//8827HWLJkSS43aUCTd+GoM1IzLDrj0UcfxerVq3Hvvfdi5MiR2LRpE5588kl4PB7ceOONPXpdmqZRVNT7wLa+ZjB3dehPyH7MDWQ/5obBth+Li4oQ4NPdLH6eR5HXl1aenA3hUASVnuHY1uSHoihdHqc724+yJIFmNeGouzEKBT3b6ZKJYzHK58HVUyYhKkrY0NQCxsT5Sp8AWDJL1ZiOHUcWiyUnn8fULCuGYfK6z+PxOLwpd6G9iVI1URSz3q5wOAxVVREVQuDFGKJCMly7pKQE3/CbjSwPXdzsbD9KkgiLLeGWyPO+GQj05vMYjWqtwNWMu8sqy2rOyH7e97qDqMLlwDB3ivOCouDz+dDU1IRTpx0GANhQW2dq+7766iu89tprsNtcKPGWG66P+ubarBrPDDT0Y4w+ZaApClarNWd/06KiIggiD16IwelwGp/DXBwf9eM7oIVu5/sY4PV6wTfWg6YpWDjO9PZ4PB5ISnKSrndY04+FcT4KSRIhSiKcTmen+7C5uRn//ve/DafIUUcdhaFDO242wFkshssIAKiEE7eioiKnZYWEgcmjjz7arqkKoefkXTjyeDwIBoPtlodCoS5bZ27duhXPP/88Fi9ejJkzZwIAjjjiCFAUhYcffhgXX3yxkQRvBkVROtyeQoFhGGOfmQ2XJSQh+zE3kP2YGwbrfiwuLUVTc3rQa3MsjuKSYrS1tZkaq6mpCQcVH4ttTd9gz549HZ4futuPoXAIjF0FxUoIBoOmtyEf6K6BIS4n7CwLu0s7bW9ubYOiqlm/Bz2MN7VUjaUsgKoJGKmIEg+r1ZqTz6Pf7zd+lmU5r/u8vr4eXkvy/VsZBnaOQ11dXdbbpXe8evub5zCy7GB88O1yYznDMIjxEcT4aNo6ne3HYCgIzq4tb2lpGRCfx3yQi+Njc3MzAEDNcCCoDINQKNTv+17P+9rc3IZSu80oQQ3wAhyVTjRv3YoJ048ERVH419ffoLU1+1y4uro6AEAsHsalF/0Of/33y9i0/UtMm3g8duzYOejOM/rfTncSUtCEwlz9TVVVBS/GIYg8WI41KiVysR9ThaNCOCepqgpJVSEpKhiG7tH2CFKy9FOUtZsS+uc9LkQRF7RzmsPh6HQfvvTSS3j11VdBUYCqatm3N998c4evx3Fcu1I1luPSzj09wePxEMfSIGDMmDH53oRBRd6Fo9GjR7fLMhIEAXv27Omyvdy2bdsAABMnTkxbPnHiREiShLq6uh4JR4D5bj/5QJblAbGdhQ7Zj7mB7MfcMNj2Y0VlJbbs3oltbQFUu51oi/PYG4qgYtJIU+9TURS0+VtRPmIYrJwNTU1NXa7f2X7keR6sWwXNqojHYwNiX+t3yjI7ktGgoChK1u9Bz3FJ76pmAaC2cxyJsgiO43LyeTTWp5JNLfKFv60VPkt6ZofXZkVra2vW26WX0F981G0Y6huN8w+fj9/9/QoEg0Gtc1A8YjiO9ByuzvZjPB4DY1fBsJpAOBA+j/mkN59HfYKe6ThSEh24+nvf606ORes3oNrtNEpQa3wejPV4IIoiSr1eUBQgShLa2tqy7pSki81zzl4IAPjBET/CeTPn4a8fvwS7zdbpfpRlGbFYDCzLGp/dgUBH78XMsVHniy++wGuvvQ5VUcFyLK677lrU1NSAZVmIIg9R4mFxcca4OT0+5mi83kLTNCRVhawqYBjG9PZwHGe4jABAkLTvnd5BLS7EEBc0Yd3hcHT6nkOhEDylCqb+uBYb3i1HKBTqdFssViuoFEcJJUmwWG1535cEwmAkvy1OABx//PH47LPP0lTtDz74AIIg4IQTTuh0verqagDApk2b0pZv3LgRADq1NBIIBAKhf6iqqkJtMIwF//ovdviDePqrTdgdDJk+Pvv9fsiyDJ+jFD5HqeEeMEs8HgfDqmBYBbF4rPsVCgA9PLWdcESZC1YVRREURYOmkndQWZqDCk0oSnuuZK49fTaoyH/JQKDNbwRi63gtnCmXcTislaaNKj0IVs6OCs8wOKwuhMPhRBZKHLG4Jhx1F6TL8zxoVgXDUgOqy99ARBdgMx1HCsPkpYzB6XTCabfj9JHDjRLUP5x0DBrCUWOSXe7zoiwhFpk55unlPS+8/QgeeOFGLPnzbzGiahxqG7bDH/B3ut7Nt/wC559/Ps4991zU1tb2/M0NUL744gt8++0muOJV2LDhG2M+YbFYIEoiBJHP/XExJbS3EDpCMwwDRVUhKyoY2rzbxmazGblGQLJUTRc947wWMg50fXyMxWKgWe38xnAyotFIp8+1WK3pjiNRhCVHXUEJBEI6eXccXXjhhXjllVdw7bXX4tprr0VLSwsefPBBnH322WmlCLfffjtWrVqFb7/9FgBw8MEHY/LkybjrrrvQ3NyMkSNHYsOGDVi8eDHOOOMM0/kZBAKBQMgtw4cPh6QouOvowzHK58GlB43DL1Z/imHDhpkap7GxEQBQ5CiHz16OxsamHm0PzwtgOAUMSyM+4IUjCoqJiYYgCOAYLq3chWUsUNUOHEd9IBwVAsFQCJ6q9E437kTpSbaEQlrwtcPqNpbZLS6EQiFUVFQAAPwhbZLfXbtpURRhYVTQDEgGQx9jdNnKmAwrNJ23fT9s2DDE4xGjBLUlFkdckgy3T7nPawT3NDU1dRnfkIr+3b31Z4/BbnMZ3/khZTWg7B0f91RVxfZt2+HwCYj6Ldi9e/eAuQHbUQlfT7JtQqEQxlVMwSXTb8O39V8YIrHenSsuRFFqyW13u0IUjmRVhdzDcHFNZEt1HKWHY/NCDHxKqVpnaMKR9p1lOBWRaLjT59o6dBzlr6MagTCYybtw5PF4sGzZMtx333244YYbYLPZcNZZZ+GWW25Je16m7ZRhGDz99NN44oknsHTpUjQ3N2PIkCG45JJLcPXVV/f32yAQCARCBjU1NQC0i3g7y0JIiCD68mzRc2WKnZUodlSioX5rj7aH5wUtHJtV07IlChl9MpE5DdInRoqiGPkRXSGKYlq+EZBwHKlq2oW+9lwhr62M+wJVVRGKRODKKFVzcRyauungmkooFAIFyuimBgAOixvhcNiYCLUGm2C1WrudeImimOjypxLhqI/pynGUy9btZhg+YgS2r/nCeLw3pE2OGYYByzAodmviJMswhnieDfokvchbjsqSpPgjKxIqSjuOcAgGg5AkGeOmBbDl4zJTr1co6LpLT+WXSDgCG6ftO4fFZQhHekv6aDwMjqvq7WamUQhiUSqa4wgJ4ch8UYrVas3IONJ+djgcYFkuUaqmCUddOY4i0QgYLtE4gFMRC3Z+o8eW6TiSJNgG2fmLQCgU8i4cAcDIkSPx3HPPdfmcBx98EA8++GDaspKSEtxzzz19uWkEAoFA6CEVFRWwWa3YHQxhWmUZdgdDYGja9J3shoYGOKwuOCwulLgq8c22f5veFkmSoMiKVqrGKQgPEOFIdxxl3kDXH2Y78RBFESydIRwxHNBPjiOqx9O53CCKIiRJQlgQsa0tYLQ/pwEETDiOIpEI7FYnaCo5qXJwmuNILzHavX8r7Lau3UYAIEkyKFpzHOVLvDhQMG48Zk6GaRpinvb9iBEj8PE/P9Im6RSFXYEQLByHeDyO8iKfIQiXFxWZEnL0/KRAqCVNOAqEWzCpdFSH6zQ0aE0MbG4JdrdqPB4I6CL6vnAEMUnKWkzPJByOwMeVAgBsnMPoxJcUjiJ96sQsBBFJdxwpitqjfWi1WiHJIhRVAU3RhnBks9lgs1rBi3HwYnruUUdEo2EwnLY/GE5BJNa5cGS32UDFkudzShRhtRDhiEDoC/KecUQgEAiEwQlN0xhRU4OdAW1ivisQwtDqKtMX3/v370eJawgAoNRVhUg0bJQMZYseGKuVqqmIxwdWpgyV4TkyW4jRleNIkvtOODJKRvI8J9L//n/a/D0W/Ou/uGX1/wAA2/xBtLW1Zj1OOByG3eJKW2bn3AiHI0aOx3e7voKvyNftWLIsg6IBilKJcNTH6PtXzShVU+n8OY5GjBgBXpJRH9byW3YHQxg+fDgaGxtRmdIGfUiRz5SQo7c+b/In26Krqoqmtv0YMmRIh+vs27cPgCYcWVw89u2rM/1+8oUucDy7YTN2+IOQZKVHpWqRSAQ2ThN8rayznXAU5yNg2dzeby+0UjWappOlaj3IONKdqrpgpOcdWSwW2Ox2CELcKFXrynEUjUbBcHrGkYJYrPMbPXabLaNUTYTDPnDC3QmEgURBOI4IBAKBMDgZMWoUtn7+GQBgdyiCEZMONj3G/n31KHVoJQIlrkoA2kRn/PjxWY+hl6Zp4dgq+LgAVVV7NMHoT4xStUzHUWJBtpMNSZLaC0cMByXDcSTL2h37nJeq5Xlf63//nx96ECYUFxn787CKMvyzPvvMrHA4DDuXLhw5LG40hvajqqoKDz30EMLhMIYPH97tWIqsgKZVUHR+u80dCCS7+2V8BmkasomQ+VwyYsQIAMCuYAjVbhd2hSIYOW0idu/ahTEpwlFlcRG2Jsp1s8Fms6GkuBQNLcmAa3+oGbwQ7zRfrra2FlYHBc6qwuEVsHvPrh69p3xyxSETMMrnAU1TPTrW8HEeVq8mZlgZuyE2G8KREDN+zhWFJhxpjiMFSi8yjgBAlAVYWTvExE0Ji8UCm9WGuBADL8RBURSsVquxjzOJxWJwleqOI+1GT2fna6vVCjYjHNuehXDflzQ2NhodOPsbr9driMfZcs8992Dz5s149dVX+2irzLN06VIsX74cq1evzvem9CkXXXQRJk6ciDvvvDPfm5IVRDgiEAgEQp9RU1ODjz74ALKqYk8ohMNN5hsBwP59+3Fo+UQAmuMI0HKPeiIc0ZxWqqaqWq7MQA2B7pHjKKNUjUk4joQU4Uj/OVfCUdJx1DMXQK7QM25qPG6MKUq2NS+yWU2JNpFIBHYuvcTCxjkQ8WuukcmTJ2c9lqwo2h+SIsJRX6OXfKoZ5TcqTUGS8rPvi4qK4HG7sCcYxtFVKvYGQzixpgZffvEFZowaYTyvsqgI/9m8xdTYw2uGYV/TLuPxvqbdALRoiI7YsXMHHD7NHeIsFlG3qRmxWKzbzoCFgH5cqXQ6YGfZHh9neCEOjtGOexbWhnjcDwBpYlFfCkdmumT2FTRNGxlHdA+EI8NxJPGAVXMeWSxWUBSldVxLlKpZE8s6Ix6Lw5twHLGcAkVWOj1fW61W0CnCESNJec3oa2xsxBWXXw4+T7l1Vo7D8y++aFo8IhCygQhHBAKBQOgzhg0bBlGWsbXVjzAvZOXESEUURTS3NqF0lFZi4bC44bC6sX///m7WTMcoVWMVMBxtLBuowpE+3TCTccTQ6ad8luagKApEMSXMtI+EI0pRwPQgMyNX6MIMm7ENDE1BMikc2dh0x5GNcyIS6bxddGcosgKKVkHR3U8aZVk27mBbLBYjAJmQHcb3pH3KPFQlf6JdTc0I7GlrQWM0Bl6SUFVVBX8ggIripOOooqgIgWAQPM9n/b2sqanB/z750nhc27gTFs6C6urqDrsIfv/9FjgrNXHdVSJAVYHt27fj4IPNO0T7m47KYXsiHgmCAI7V9i9Hcwjx2rGwL4Wj1O99oTiOFFWFrPbseJ3qONL+52FJ7DOrzQpB5CGIcaNzYEeoquYwYrmk4wjQyteyFY66Gr+vCQQC4EURVytRVKv9e2ypoxg8LToQCASIcNQLZFmGqqo5L00dDJA9QiAQCIQ+Qw/CXlOvhbtWV1ebWr+xsRGqqqLYWWksK3FVmA5vNUrVOK2rmr5Mz6UZqGQ7QZIkCSydftHN0qyWcSQl74zmWjgyyh1kBRSV/1jF2lAYI7xutMV5REUJUFVTE7ZoJAYn50tbZuMciMc7D2/tiNTQcyoLx9FTTz2Ff/zjH8bj//u//zMtwh7I6Puba2qCmpjIihWVoEQRQh7dXsOGD8e3dXtRm+iopnfmK/f5jOeU+7RjVHNzc9bHzxEjRmDVqlX4fs8GDKsYjd37tqKmpqbD8qO2tjY0N7Vi4iHad99RJIJhKWzdunVACEe5Ely0Y6T22WBoznApHmiOI1lRoagqaMb8FDEpHPGJ/0VYEkHVNqsVfCwOQeSNZR0hCAIURU3LOAK0Gz2+lO+FjtVqTcs4oiWpIG4IVasyRqCf/6a9/Cp8+umnWLRoEWprazFy5EgsXLgQhxxyCADt8/nSSy9h1apVaG5uRkVFBc477zxcdNFFxvp6ednSpUvx8MMPY8uWLaiursb8+fMxffp043nZjKXz7bff4pFHHsG2bdtQVVWFG264ATNmzEh7zsqVK7FixQrU1dWhuLgYZ5xxBubOnZu18HPNNdfAbrdj5syZePHFF1FXV4dnn30W5eXlWLJkCdatW4eWlhaUlZVh5syZmDt3btpn7KijjsL111+PWCyGN998E4qiYMaMGVi4cGGaa/Obb77Bo48+ip07d2Lo0KG44YYbOtyejz/+GM8++yx27doFl8uFk046Cddff71xfli7di2uvfZaPP7441i1ahU+//xzuN1uXHvttTj99NPx2muvYfny5YhGozjppJOwcOHCnH0niHBEIBAIhD6jtLQULMPg66YWAEBlZWU3a6SjdxMqSRGOiuyVpoWjtHDslAvRgYrZ60NJksBQ6ZNGhmahKHJ6+2QxGWaaC/TgWkqWetTeOVfo2/Hkug2ocjnx2nfbsL6xGXMOHg/FxMQzFouhiE0v37GydgiiAFmWs84F0QOZsw3Hrtu3D7LTieAxx6Hog3dRX19PhKMeUL5iOQBAZRjULVgIS0MDAqz5kpxcUV1djY9CEewLR8GxrCEklHk9xnPKvD0TjgDgd8/fgF/NeRK79m/FIYd1XNq7ZYtWBucp0777NA24SwV89913PXpP/U1HbjKzYpLWJCB5jGRoBrKYcCmmTD4PBMeRlnEE0D34XhjCkZLiOEoss9qsCAXjEMQ4rNbOzy/GudqS7jjq7Hxts9lApdz8oEQxr46jgUpzczMefvhhzJs3D263Gy+99BLmz5+Pv/zlLyguLsaiRYuwYsUKXHbZZZgyZQq++OILPP7444hGo7jyyiuNcSRJwt13340LLrgAV155JZYtW4Zf/vKXeOutt4wbdWbGuuOOOzB79mxUVVXhzTffxK233oqXX34Zo0ePBgC8/vrr+P3vf49Zs2ZhwYIF+O6777B06VI0Nzfj17/+ddbvf/PmzWhoaMDPf/5zuFwuVFRUoK2tDR6PBzfddBPcbjf27NmDZ599Fi0tLfjNb36Ttv6f//xnTJkyBXfddRd2796NP/7xjygpKcF1110HAGhpacH8+fMxevRo3H///QiFQnj44YfbuZX//e9/47bbbsPMmTNxzTXXoK6uDosXL8aePXvwxz/+Me25Dz/8MM4++2yce+65eOutt3DPPfdg+/bt2L59O2677Tbs27cPjz/+OKqrq3H55ZdnvS+6gghHBAKBQOgzGIZBSXExtjQ1weNymb6ga2rSgovDvB9uWxEC8RawNIf6+uzbUwPtw7FTlxUyyRDsrn/fHZIktStV04QjBUIflqrpQgqlqmB7kJmRK/QJ37VTJmGUz4Orp0xCVJSwrqERNhMiGR/nYfWlf4YtrPY4Ho932WI6Fd3NQNMqKEbpVjjyB4OIjRmHyKSDUfTBux2WGxG6p/HCiw3HEQAIFZXwyPnJIgE0IZ2XJHzX2obK8nL4/X4AQIknKRwVe9wANGdQttQksuR+dOLlGFY5Bq3BBgwffkqHz922bRssNkCI0+BsCvgoA87J47stm3v4rvoXXXChE8oRBcq0eyfZhEATmGmKMcZIFdH7UjgqhJwzo1RNUXok9CczjrTziCQnO3RarVa0SBEIYtcll6k3eVL/17vcdfiaQjKnj5LEvGYcDVSCwSAeeOABHH744QCAqVOn4pxzzsGKFSswe/ZsvP7665g9ezauvvpqAMD06dMRiUTw8ssv46KLLjLcMKIo4tprr8Wxxx4LQBPHzzvvPHz66ac4/fTT4ff7TY01Z84cnHPOOcbzZs2ahRdffBH33nsvZFnGc889h5kzZ+LWW281ngMATz/9NObMmZO12B4KhfDiiy+ioqLCWFZSUoL58+cbjydPngy73Y7f/va3WLhwYdr1bElJCe655x4AwNFHH43Nmzfjn//8pyEcrVixAgDwhz/8AW63u8PxAeDZZ5/FQQcdhPvvv99Y5vF4cOedd2Lt2rWYNm2asfzkk0/GFVdcAQCYNGkSVq9ejffffx9vvPGGcaxau3YtPvroo5wJR/n3jRMIBAJhUFNcUqz9X1xset3m5mZYWCv+8OF81Pq34421f8S6Pf9Ca2uLqXHShKPEhehAEI501EyPkcm706IogqbaC0eANnmRZU240N1HuXIccRwHe+IisDilU1R/o7+fErsNdpZFlcuJMUVeCIpqSszkBd4I0NXhGG1sIWXy0h2GcE3THO0AAOtxSURBVMSqoGi523WDwSAUux1gWVAch1AolPVrEZKI5RUQKyohVmgORtXCgctjjoU+SdnY1IryykoEg0FwLAtHysTXYbWCY1lTYqHNZkNpaRkkSUQsHu6yo9rOnTtBcxLWvzUE4RYLtn1ahKbtLjQ1Ng+IY6QuuDA0ZfzfUxGmLdoIXowhJoYhJY6JxcXF+MlPfoJTTjkFhx12WG42OkHqdhaCcKSFY6taODZtXujXJ6uS4ThKF45EiYcgCbB2ccxN5hFq5zjW0vWNHpvNBjU1iFoQiOOoB7hcLkM0AgC3241p06Zh48aN2LhxIyRJwsknn5y2zqmnnopYLGa4FgHtM3TkkUcaj4cNGwaO4wz3uJmxAODEE080fmYYBscddxw2btwIANi9ezf8fn+HY6mqiq+//jrr9z9mzJg00QjQBOVXX30VP/3pT3H88cfj2GOPxZ133glZllFXV5f23KOOOirt8ciRI433rL/vadOmGaIRoIlcqTebotEotm7dipkzZ6aNNXPmTDAMg6+++ipt+RFHHGH87HK5UFRUhKlTp6YJ3MOHDzft0O8K4jgiEAgEQp/i9RUl/veZXtfv96PIWY5Lj/oVyt1DMWva9Vi3ZzX+sWEZ4vGuQzZTicfjYFitNGggOo4y0WUjOssAU0mSO+iqlrwEECUBDMP2iePolZdfRjQaRVEehSP9DmY0w9kTEyVTnaM66k7H0eaFI55PlAUxWuZWLN7x3XSdcCikCUcAVIcjb62eBz6ZAmzPgpRzRVlZGQCgjedRVl6OSCQCl92etk0URcHlsCMcDpsau6qqCo1t+9DYtg9A5/ly++vr4KmI46CZITi8EsYc0wb//ii+/08JGhsbC74k0hCOEvuMoWjTIoy+v9/b9ArGV07DtsZvoLDad5RhGFx11VU53OIkhSYcaaVqWsZRtmW3qegikZRw8UmyAIvdYvxOkASIIg+LtXPnVjvHEdt1abnVagUUBZBlgGGgisRx1BM6yo8qLi7G3r17jRsVJSUlab/XH6eK2lartZ0zj2VZ4/xoZiyWZeFJcV/q29TS0pL23GzG6o6Ork9WrFiBJ598EpdccgmmTZsGj8djZC5lnu8zG1akvmdAK1XrSLxPfd1QKARVVdu9H5Zl4fV6272fVBEK0ITbzO3gOM7UtUl3EMcRgUAgEPoU/eTmzrgAyIZAIACvrQTDisfCytlR7h6KmmItq8PMRUEsFgPDJSYW3MATjuQMh5Gipv++O2RJAkO3zzjS2V63GXE+isYW7S5aLsNFHQ4HSktLezQRyRVWqxUMTSMipAtHYVGEy519hzJRFBDmA9jb+j1q27YBACK89jk0M/HTJ0GsRQHLqZ2WYQDa51QSBMh2TfxS7A5SqmYSQ2DNNOqpKqg8dvtzu91Ghk5xcbHW6ZFjsaW2FlGex96mJmyprYWNs5g+XlVUlKMlUI/WgHbXu7MuSwF/ABSV6FwVYOHwSrC5tO/JQHC26e49vWMix9Ddln5moh9HT5k4G0N9ozGqdBI87r5vnJC6nYUSjq0omuOoN8KREY6tiOAsnPE7UeQhSHyX55ekcJR9xhGgZRtBlgFFIcJRD9DLZFNpbW1FSUmJId60tram/V4XcDLFna4wM5YkSe3Odfo2mR2rOzq6lvroo49w3HHH4brrrsP06dNx0EEHmbrRlEpJSUm77QTSS5Ddbjcoimr3PEmSEAgETL2fvoI4jggEAoHQp+gXdj054YZDYdi49Im9w6IJUeFwOOuWszzPg004jegB5DjqbMKrQgVtwikhSTLYzFK1lLDsR1+6Gb+a8yQ++nIlgIT9vwDCWnMFRVHwuF0IZtx5CwkiPJXZTxAFUcDHW9/Ex1vfBENzeOyCv+Pzne8BMCcc6e4RhlPAWBSEmzqfoOvuIkXPfbDZiOPIJMb3KHNyrqp5DW2nKAo+jwfNra0oKirCvn37EOcFzHvsSfzx+muw7IOP8OWWraipKDcEklSeePwJ7Nm9p93ysePHoqSkBOtCX6Mt1Ayns/N8uWg0hnirGw3fu0HRKqaeU4+6Te4On1uI6PvFmhA6LDRtOPrMwDAMfI5SWDk7rJwDVq7vxYfUY0ZHf9/+hmEYyIqS6KrWm1K1pONIz5DjOA6iJECUBHitnWfBZZaqUTRAM904joC0gGxSqmaecDiMNWvWGOVq4XAYa9euxXnnnYdJkyaBZVl89NFHmDBhgrHOhx9+CLvdnrasO8yOtXr1aiPjSJZlfPLJJ0a3x5qaGhQVFeHDDz/ESSedlDYWRVGYMmWK6f2QCs/z7dxT7777bo/GmjRpEt544w2Ew2HDFfTZZ5+lhWM7HA6MGzcOH374IWbPnm0s/9e//gVZlnv9fnIBEY4IBAKB0KfoF3Y9cbFEozF4uNL08RJdrbpyaWQSj8dBc3oAKsCwA0M40u+CKch0HKnaG8kSWZZh7cJxdP0F92J45RgcM/lU7KnfBqvVOiD2jxm8Xh+CfLpwFBBFjDBRQknTDI4fey6mDT/J+NscPfoMbGlYZ8oxoN9F5WwKOJuC1lDnZUiGcJQQXiWHAy0d3B0mdI4R0q6kf5MoVQFD5zbw2CxsYmLidruhqiocVitu+PHZUKHix8dMx1VnnoZ7lq9oJ+Sqqor33n8fI0omoMw91Fi+P7ALH+z+EJf+7BIEI36EIn74vJ2LoxaLBfE4j3HHtWD/d06sWzUEE05oQuteR7tSiEJEP05ZEgKglaF7VJrB0AxkVXMAyYoMhut7h2SqWGTWJdUX6BlHPS1VYxgGFEUlS9UUARZL8vwvSWIiMLvzsmUjj9CSPJ6yFqpTMVAXiWhRhL4GcRyZx+Px4L777sNVV10Fl8uFl156CQBw4YUXwufz4YILLsDy5cvBcRwmT56MNWvWYOXKlZg3b56pm4JmxuI4Di+88AIEQUBVVRXeeOMNNDY24rLLLgOgfd6uvPJKPProoygqKsKMGTOwZcsWLF26FGeddRaqqqp6tU+OPPJIvPbaa/jzn/+MYcOG4b333kNtbW2Pxrrwwgvxl7/8BTfddBN+9rOfIRQKYenSpe1cRHPnzsWtt96KX//61zjzzDONrmpHHHFEWjB2viDCEYFAIBD6FP0ClO1BCC3P8+DYzDBi7bHZTBmGSU68WK7zC9FCQndKKO2ysdWs840AbVKSGY5NpziOyoqHwGZ1wG7T7oRpk8nBJRz5iovR2liftqw1zmOqiewliqLgs5diWPHY5LiO0i7W6Bi/3w+KAjirAotdRjQSgyiKHXZt0ksIdMeRYnegbX9du+cROseYBGe66GSlR8elnJIQIF0uFxiGQSgWxX3LtQ48LMNgyfzrIIpSu4m8JElQVQUTKg/HSePPQyDeAl6MYUfTRvz1m2fgcrkgSgKCkTY4XZ2XY06ceBA2bf8PykdF4a3kIYsUgo1WUJTW9a3Q0Y9TtsTf0UrTnbpTuoJlWciKLhyJ/fK5KDThiGEYSIoCSVFh6YFwRFEUOJaDqIdjKyI4LnlOESQeotTxcU4nHo+DZoDU01tX5bxJx5EEKtFZrxAcR3UU0740tj9es4eUlpbiuuuuw6JFi1BXV4dRo0bhiSeeMMrCbrjhBng8Hrz11ltYtmwZKisrMX/+fFx00UWmXyvbsViWxb333otHHnkE27dvR1VVFR588EGMHZs8/55//vlgGAYrVqzAypUrUVxcjEsuuQRz587t8b7QufLKK9HW1oZnnnkGAHDSSSfh5ptvxs0332x6rNLSUvzhD3/AY489httvvx3V1dW45ZZb8NRTT6U97/jjj8eDDz6I5557DgsXLoTL5cJpp52G66+/vtfvJxcQ4YhAIBAIfYoucJgROnQEgUdUCWFv6/egKApDi8YgFNfqv81caAuCAIpNlgXQrDrAhKP0K1BZTXYRygZZlkFT6fs/LRxbTGRSSDwYhslrHlFfUVpair17k2U9iqqiLRZHaWn2wg9NUe063Ol/GzOf75aWFljtQLiVM0JgW1tb23V1AZKOIyPjyOFEkJSqmSLpOEovJ6QUOa9d1YCkq9DhcMBisUCWtc/DbRech7c//wLzHnsSJR5POxeFLjr8Y+NLGFsxFe9vegXf1a/F+YfPhyRLxuQ5GG6D3dX5RPqQQw7BmrWfg6JVOLzaMbVukxs1I4bnNOusr9AFBXvi72hnGcRMuFF1WJY1nDKyIoHl+lc4ymWAbU9hGEZzHAE9PgewLJeyH5MCnFaqJqZ1WuuIWCwGlks/tzGs2umNDEM4EkUjuDefjiOv1wsrx+Fp0ZGX17dyHLxdOAw74s477zR+njFjRofPoWkac+bMwZw5czodZ968eZg3b1675atXr+7VWMuWLetq83Huuefi3HPP7fI5XbFkyZIOlzscjrR9o/P55593+RgALrnkElxyySVpy6ZOnYqXX345bVlH+/vEE09M6yaXybRp0zp8zVWrVrVb1tnfpKcQ4YhAIBAIBUsoFEJdaDXW71ltZMp89N2fTY8Tj8dB0clJI8MWxoV6d+gX7/vDEWxrC4CigNE+LwI8b9xdzQZZlsGw6ROBVCFJSHRTEyUBVsvgtPmXlpbiw5ZW/G+f5jryx3nIitKug0lXaOGx6eKDknApmJloNTU1QYWCdauGYPzxzcayjoQjv98PymYDEhMw2eFAPBLp1KFEaI+xn+T0ckJKKQDHUQKbzQaXywUVwNJf3IhhZWWYPHokojyPG5/6v7S2zUBSOD9+7E8AVcWMMefgrMlXYkv9WgBJh2c0HkZpSeclZ4ceeihkCQg0WFFUxUNVgcA+F445Nf9lEdmgC0fOhNDj5Di09iDUm2U5w3EkKRKs/SAcpZ6DCuF8pB/DBEWBp8fCUYpzS5WM754mikoQxfa5ManE43EwGb+mWaXbUjUqRYTLp3BUXl6O5198MW85dF6vN+vsRwLBLIVxtiQQCATCoKcnYctujwejfYdh5sQLjDvzJ42fhW9q/2OqjTYv8KDZ5KSRoju/EC0kdBfL8s3fY/nm78HSNFb++DSsqW+CbCJTR1GUtNI0AKBShCMxIRwJIg/LIM2HGDVqFADgd5+tM5ZRFIWampqsx6BpGq2RevBizCgN0u+umxEg6hv2w1kSx6RTA7DYNSGqsbGxw+f6/X6ojqRooJesBQIBU26pAxn9b0NlBJjTslww4hvHcfB4PIgLAkZUVMDKcXCUlYEXRcR4vl0Whp6p9e/vV+Lf3680hPW/rPsjgOSxQxDjYNnOyzFHjRoFr8+D1j1BFFXxCLdwiEeAI444oo/eaW4Jh8OwsazRVc3FcYhEOs8M6wyWZZMZRymCR19iiEUWS0Gcj3ThSOxFCSfHcUY4tqxIaY4jAIjz0S73Lc/zRjC2Ds3KWYRjS9DvpeS7VK28vJyINwWCLMtdXnsWyo2DgQLZWwQCgUDoU/QJTk+EI7vNDjucaZkybps2CTJTRiHwPOiUjCOaVQviDm936JO/n04Yg+lDKow87EPLS9BQm30XHkVR0oQiIMNxZJSqCbBwXe/X3/3uPnz99dcAgNmzL8GPfvSjrLcjnxx33HH485//nBZizXGcqWBPWZbxn21v47CaHxilQVfOuMsYK1vq6/fDUSHBXar9DS12Cg0NDR0+NxAIQHIkt1FOiEh+v58IR1mi/20oOb28lVbkvE8c9OMiy7IoLi4GALQEg6hKOOFagpp7Rv9d5no/mXo1RpdNNoT0KUOPw67mb43nKarSpchO0zSmH3U0/vP5uwD8aNljh81uNToXFTqhUAgua/KY5bJwCDU0mx5H6yimCYuKIoFhetZ22wxG+ZXdUVDCkSDLPSotB9IdR5KSdEXq37MYH+s+44jNcAYycqf7R78OSHUcDYQSS0L/MGvWLOzfv7/T33dU8kXoHCIcEQgEAqFP0Sc4ZrpO6dhsVvDh9GwDQdYuIM3Y0XkhQziilQEjHFEUhVK7DWOKkrkFTo6DxZK9UNFRxhHdgeNIlIRux924aSNoZzPiYQ7ff/991ttQCLi6CAnOBo/bi4Mrj8VQ32jMmnY9eDGGxpDWZSXbyYosy2hpbsXosUkRw+6WOr24bW1rg2hP5mWkOo4I2aFPVLmmRkhFxWAiYVCCAFqU8j7J1EUdmqYNl0JDm98Qjhrb2gCgnYNBX6/ENSRNWHdZzeWbAMBRRx2F9957D7EgC3+tC4dPO7xgnFjdEQwG4Un5G7otHEKRiOayNCF+MDQDRU0IR6rcLzlvPM8DNA3VZiuIZgT6e+Z7UcLJpZT8yUrSuaX/rzngOh+b53nQTLozkGEVxPnuHEeFUapGKCweffTRtCwxQu8gwhGBQCAQ+hRdOOqJ48jpdCLSFklbFhe0xw5H9uGToiiA5pKvTzHygLmY0O6Ep4tusslJkaqq7RxHVErpmihpYlw2pWqyJKN4ZByB/XRBdALqTxxOB6ysHVbOjnJOa4Fe598OIPvyiMbGRiiKCps7ue8srjj21+/r8Pmtfj9kd7JMSSbCkWl0caj43b+j0VcEz2efwrZrJ9TS0rwLRzoURRni0P7WVkzF6MTPXQtHipp+bMgMb6cputtj7+TJk0HTFJp32RFsYnHYhQMj3wjQvgeelDwij8UCRVEQiUTgdnee7ZQJRVNQE/tSgWqqFLqnxGIxUBYLJI4rCOFIF3R4qefCGcMyyVI1Weywq2pXwlE8HgeVIRzRXYRjsywLmmEKJuOIUFiMGTMm35swqOiZD5FAIBAIhCzpjePI5XYhKqYHneqPzbhHBEEAnXIdTNHKwBGOaBpyZhdxVQXDZH/vR8s4ynQcaRMjlmHTS9W6mUhLsgyKVkHRygEnHNntdvBS+p1vXorBwlmynmjV12vh3KnCkc0tob4Tx1EgEICS4jgCx4HiOCIcmUD/TLeddDLE8gr4Z56ChksvByX1T5ZNtlgsFpSWlKC2KVlqVdvcjNKS4naTYX3ynRnWrrs9kt0smW6PvU6nEyNGjkDtRjdUFQOmTA0Agn4/PCkuSW+ibG0gfD9isRhgsULiuE7bzfcnqaVqPe+qxqaFjOuf01SxqKtzTGYeIQDQjIp4vGPHEQBwFgsoSQIlSqAZJu/lpwTCYIUIRwQCgUDoU3riNNLxeDwIxluxt/V7oyxob+v3xu+yRRRFUHRKqRqjGmJJocMwDOQMV4Gsqj1wHKXfQdcdSCzHpZSq8bBauxaOZFkGTQOgVMgZYcODHYfTjriYPoGJi1HY7dm73/QQbJsrRThyyWhtbetwf4aCQVCSCEoQwLa1gmuoh2q3IxgM9vBdHHjooovidkG1WCAVFUOsqAQly3kP0tXRxZ3qoUOxN0U42tvUhOrqoe2er0+OdXeHjqRIYBnWOO7SNJPVMXjc2PEQoiwsFg7V1dU9fh/9TcDvhyflmOXpoXCkKklXJg2qV+etbIlEIlCsFigWK8KRSPcr9DH6ZyouST0WX9iUrChZSQpQqQJtV6IUz8c7CMfuOpPQYrVqwpEkgisQByGBMBghwhGBQCAQ+pSamhrQNI2RI0eaXtfn88Efbcaj71+LWv92vLH2j3hv0ytwOlymLmwFQYDEU5BFCtEAC0mkIRRAGGk2MDQNSUm/kJYV1dT7lyQJoZg/Q3zThCQuTTgSurX5y5LmOKJpFZI0MFxbucLpdCIupXdsiotRU2WTLS0tsDqoNAec1SlBUVS0JfJsdARBgCyKcK/5ElxjA3wffYCKl1+EYrMj1IOW4wcqRg6KmOGQE8WCKWvRRcNhw4ZhT4pwtKepGUOHDWv3fH0iLsrpE2pR5sFxFkP4YLIUjnSxaEhVZY+DkfNBIBiEN0Us0H82KxzJigwmUb5L02y/uCkjkQhkixWK1YJQAQlHUi8yjhiWhaImM470cVLFoq5cfpmNLLR11S7P1xaLBZQoag5CIhwRCH3GwDkzEAgEAmFAcsopp+Bvf/sbzjrrLNPrFhUVQZJF3HTyE0Yg8dRhJ6KoqPP20h0RjcRQv9WNcIsF2z4tQlutHeIAET1Ytn3GkaQqYExM7mKxKD7b+Q9DfHv0/WuBxGSSY5PCkSDx3ZaqybICigYoWoV4gJWqOZ1OxMT0CV5MCMPpdGY9RltbGyz2dGeRxSEbv0slHNZEqraTfpBWYiVbrcbvCN3DcRwoitJadqegimLBOI504Wjo0KGobWqCrCiQFQV7G5swrAPhiKZpWCxWiHL6hFpzDSbFMIrKzj2jd+hzu7J3cuYbRVEQDIeN8jQAcFstoADTjjxRFMEyiQ5gNNcvpczhSASSxQrVakM4XDjCUebPZsfQS9WUThxHXYZjC+2FI5pVIXTx9zAcR6IIi6UwhGACYTBCikAJBAKBULAYkxlbkRFIrKgyysrNtSGnGRpVB4XgKhEw5pg27Pxy4OTz0DQDWW3vOGJMXNjbrDZMqToprRtYslQtJeNI5GGxdB4oK8uyVvZGq6BoHHCOI5fLhZiYLtjExBDcJdnnbTU1NQGUjFCzNpFyl4qQRc39lTnZ1XNPxPJKrcTKorVkl6xWRCL5z0QZKFAUBc5qBSWmuHMUBZDlggnH1o9HQ4cOhShJaGhrA0BBlCQMHdq+VA0AbFYr+IzSSV6O90gM0zPjMkP0C5lIonuaO+VvyFAUnFaLaeEoHo+DY7T9ZmGsiMfaulmj9wSCIcg2GxSbDZFw/h2EqYJOz8OxaSOwPbU7Xep4XQlHoiCAcqaf7yhahZTpFkzBarEYXdW6K7UmEAg9Z+CcHQgEAoFwwKELR/5ok7EsEG8ylmeLqqiwuSQwnAqHV4LFMXCEI81xlCEcqeZK1SiahtdRqolv7qEYVjzWKEdJLVUTugnH1l0RuuNIGiAB47nC5XIhymeGtYfh9mTfvWnPnt0INXNYt2oI1v+1EgBQ9622fmZAbiymiQJqxt9E4SyIxIhwZAaL1ZrWeUkXkQrFcZQqHAHA3sZm7G3UjnudZQ7ZbY52Ye1a5pbdeKwoSlYdwgqlZM8Mermm25Je+uS2WEyVcqqqFr5s47T9ZmUd/RJWHQgFodhtUGx2xKLRvGfGpZ5TehoazzIsZEW7wSArsnGeyVaUivMCpHiyrDzUzIGi0aXjyGZkHEkD8nNMIAwUiOOIQCAQCAVLUjhKZn60xZpQWjrV1DiSJKWHY9MDJ9iZYVhIGeHYPcmgyJw6UnrGEctClBKOI7nrjCN9ckvTKmgaA0Z8yxVutxsxIaLloSRCiqJCCC7XkKzH8Hg9iDM7MfJwv7FsxGEBNO90tguA5RO5HiqbPolTORbxaP5LWwYSNpstQzgSjeWFgP5dKi0tBceyqG3Wjnkcy6KsrKzDdRwOB+IZpZNxMQqH02FM2GVFyiqzqD/az+eaSCIXyJkhcjg5zlQpJ8/zkGQJdk5zXdktLkT8ff/9CgWDUIbVQLbbAVVFJBIx1fQh11RXV2PmzJmIx+OYNm1aj8agGRqKKkNNnLPMOo4i4QjizW4MmRDF7vUetNXZMeaYVkhi5+drq9UKKhIFQBWEcNTY2Ji3rn5erxfl5eV5ee2+IBQKYcWKFZg5cyZGjRqV9Xpr167Fhg0bcPnll/fZtomiiKeffhobN27Ed999h3g8jvfeew8+ny/teffccw82b96MV199tc+2pb8gwhGBQCAQCha73Q6nw4W2qNaJSlYkBKNtnU6kOkOWlbQwYmoAiR5aZkTvHEddjs9xaaVqXd1pTjqOtHI1SR4Y+zBXuN2aMygmhuGyegFowpG+PBv07lkOrwQ+yiDUzMHhE9N+p6PnrKgZd+hVhu3yDjyhPZnCEV1gwpH+3WIYBuXl5djX0gKKolBeXt6pQ8PpciAWTnfGxMUInC6HsY4kiVmVHfVHF7FcozvymhP/UxQw2ucFS7XPC+sK3Z3kSJTpOizuPg+fV1UV0XAYit0BJeEQCwaDeRWOrFYrbrnlll6NwTAMFFWEomqfZ/08la3jiGEZDJmYLCuXRT9CzZrjUpblDte1W62gA0EooGBz5Pf73NjYiCvmXAleiOfl9a0WG55/4blBIx6FQiE8++yzGDVqlCnhaN26dVi+fHmfCkfxeBxvvfUWJk6ciClTpuCzzz7rs9cqFIhwRCAQCISCpqysDP6EcBSItUBVFVMXRYqiGLk8OtQAchyxbMeOIzMZR13BsSyELLuq6WIbRQMUc+CVqukCUYQPGsJRhDc32Qv4A2hrtCPcYjHuqB/7s70A2k+ojM9opmOEpqEMkM9voWC32UGnOLoooTBL1QCgvKICDW1+TTiqqOh0HZfbhWBbRuaWFEaFKyk28VI8K5F5oAjpqejC0f2frQMAsDSNlT8+DU2RKIJ792Y9ju4Ocdm077TL6kUkGu5UqMgF4XAYiixDcTig2LWujH6/v9M8q4GCJhzFjZwj3e2WreNIVZS0snIAiLZpNzM6+3tYLBbQsgyVomDLs+MoEAiAF+K4dPovUeEZ3q+v3RDcg5c/exCBQGDQCEeFjNvtxgcffACKovDOO+8Q4YhAIBAIhHxTVl6GtjpNOGqLaP+byThKLa/SoWh1wEyUWJaFLKV3TpIU846jzvwEnIWDGNHGF8Suu6pllqoJB5h4oQtEUUFzI0iyCF6MmXIcDRlSBZ7ek3ZHXZG1MqHORDu1gzKigegQyScOhz1RzqKhu49S84DyQuLvmOo2Kysrw94t3wEAho4b3+mqLpcLdfHd2Nv6PcrdQxGItyDC++FyjTKcg7wQy0r86I8uYrlGL+184LijYGNZ6F+TUT4PohnlIl2hu5PcVq1bp9tWBFVV4ff7UVJSktNt1tHFKsVuh+JwpC0byNA0DRWK4TjqqFStq8+jLMvtdHI9r72zmz0WiwWMJAEUCibsvsIzHMOKx+Z7M7Jmx44dePLJJ7Fp0ybwPI+Kigqcc845OPjgg3H11VfjzTffNLLWFi5ciH//+9945ZVXMHas9h7vuusutLS04I9//CMA7bv57LPP4r333kNLSwuqq6txxRVX4Ic//GHa627YsAFLlizBpk2bwDAMjj32WCxYsADFxcXYt28ffvKTnwAAbr/9dmOdlStXoqqqqtP3snTpUjz77LMAgKOOOgoAcNhhh2HJkiUAgPXr12Px4sXYsmULrFYrjjnmGNx4443Gd11/3TvvvBPr16/HP//5TzAMgzPPPBPXX3992rVXT0p8FUXBQw89hH/+8594/PHHMWnSJNNj5AsiHBEIBAKhoCkrK8U327cAANoSIdlmStVSA511aAaQpYEhejAsA1FIdxzJitLj8FIdNSElsSyLeIrjKBvhiGLUASW+5QpdOIrwWscmXUAyIxwVFRVhTxOTdkc9FtQmUo7EBFJHvyjlmhqhJDKNxIpKUHwcikKEIzM47XbQ/uTEnC4wx1GqEFhUVISvg9pna3JxcafruFwutEYa8Oj71+LGmX/A+5tegT/aArfbbRwfYnw0q8n0QBSO9G0eU+SDjU2KEU6Og5loa0M4smnCkcdeZCzvK+HI7/cDAGSnUytVo2lT5XWFCk1rXdW6chx1LRwpae5gAMbjzs43FosFlCyDpqiCEY4GGrfccguKiorw61//Gk6nE7W1tWhsbMSkSZNgtVqxbt06VFdXQ1VVfP3117BarVi/fr0hHH311Vc4++yzjfFuv/12fP3115g7dy5GjBiBTz/9FHfddRfcbjeOOeYYAJpodM011+CYY47B/fffj1gshqeffhq33HILnn/+eZSWluKhhx7CbbfdhmuuucbI3eruxuE555yDxsZGvPfee3jqqacAAE6nEwCwefNm3HDDDZgyZQruv/9+BINBPPXUU7juuuuwbNmytJs3S5YswZFHHonf/e53+O677/DMM8+A4zhcd911Pd7PkiThnnvuwZdffoklS5ZgzJgxPR4rHxDhiEAgEAgFTWlpKfzR/wIAArFmOOyOdhPsrkiWV6U4jigV0gBxy7AsBznDXSJBhd1MVzXAcDbo6BNVrataFKqqQhCzE45oGqCZA0840gWiqKAJR5HE/2ZK1TweD6R4uugnxpkOx9EnXWVv/hmAlnVUt2AhrHW1iGXkIRG6xm63g+0gHNvMsaQv8Hq92F9fnyYE+3w++BPhzl6vt9N13W43QAG3nLoY5e6hmDXtejz0j6vgcrmM8WRZykpkHsjf5V2BIGq8brTFeURFCRS0tu7Z0tLSApfNC5bR9pPXXmIs76uJnS4SyQ4nQFGgnE5DTBrI0DQNRZF7LBwpqopME4f+ODMDTsdisYCWxIJyHA0k/H4/6urqsGDBAhx33HEAgMMPP9z4/UEHHYT169fj7LPPxo4dOxAOh/GjH/0I69atwwUXXIB9+/ahvr4eU6dqTUvWrFmDTz75BE888QSmT58OQHP+NDU1YenSpYZw9NRTT2HixIl46KGHjJsko0ePxuzZs/Hf//4Xxx57LMaNGwcAGDZsGA455JCs3k9FRQXKy8tB03S7dV588UUUFRXhD3/4g3FcHD58OObOnYsPPvgAZ511lvHc6upq3HnnnQCA6dOnIx6P49VXX8Wll17aoywyQRBwxx13YMuWLfi///s/DB/ev6WMuaD7NgsEAoFAIOSR0tJShOMBiLKAQKwZxcXm7gCn5vLoULQ6gBxHLKSMC2ZZ7friux0UZTiMMtGEIz7RQlnJMuPowHQccRwHm9VmOI70/81cRBYVFYGPpV9+CTHG+F0q+iSo+ewfo+HSy9E4+1Lt+eUVKOrCiUJoj9VqNQKxgcLJOPrlr36F+++/P61cweVygRdF8KIIl8vV6boejwdxIYKhRWNg5ezw2UshKSI8Hk/aBDqbyXRyUj7wnGwLP/4fdviDePqrTVjwr/9CARAIBrNev7W1FV5H8rzishaBomi0tLT0wdZqtLW1AQwDNfH5kx3OQeM4UqH2KuMIGY4jZCEcQZZBSTIRjnqA1+vFkCFDsHjxYvztb39DQ0ND2u+nTJmCdeu0HLH169dj/PjxOPbYY/HVV18B0NxGHMfh4IMPBgB8/vnn8Hg8OPzwwyFJkvHv8MMPx5YtWyDLMuLxOL755hv84Ac/gCzLxnOGDx+O0tJSbN68uU/e61dffYUTTjghTUw/5JBDMGTIEOP96Jx44olpj0866STE43Fs377d9OvyPI+bb74ZO3bswDPPPDMgRSOAOI4IBAKBUOAUJybIwVgrgrFWlJSYmzAbLhkmeTFKM4A0QIQjroOuapKi9qBULcNxhBTHkSxCTJSrdXXhrZeG0IyWcTRQxLdc4nZ5jBI1/f+uJveZlJSUQIipkCUKDKv9DfgIA5qh27Xx1UU82euFWFFpLKdoGm4Tr0nQHEd0huOIYdk+Cz/OloqKClRkBGCnuqD0EouOcLlckBUZvBSFjXOmlU6mCsDZtCjXs57c7vx19TKLLkA8cNxRGOXz4OopkxAVJbyxdbupErOWlhZ4rMnzCkMzcNt9aG1tzfk26/j9flBOp2GnER2OQSEcMQwDQeZR17YNQMfCEZ0ZYpRAVVWoqqETGehu4c6EI47jQMkyQNG9LuE+EKEoCk888QSefvppPPLII4jFYhg/fjwWLFiAqVOn4rDDDsMLL7yAhoYGrF+/HlOmTMGUKVMQCASwc+dOrF+/HgcddJBxnAkEAggGgzj22GM7fL3m5mZQFAVZlvH444/j8ccfb/ecTPEqV4RCoQ6PDcXFxQhmiM2ZN3L0x83NzaZft62tDQ0NDTjvvPNQWVnZ/QoFChGOCAQCgVDQ6MJRKN6KIN+CYcXZB2MDSbEjtVSNZlSoqtqnXXNyBctxEDMumCVVMbXdNEW3D1NOLVUTeYiiFpCdjXBE0arWVe0AFI5cbhciGcKRmYwjXSTgwwwcPinxM4uSkqJ2EypdQKAzym4oQYDDTRxHZrDb7aDE5H6kRQFWW56DsTsh1QXVleijl7FF+CBsnDPNAWdWODrqqKPwzDPPmMqPyzf6sara7YKdZWF3adMaUVHhNCGstra0wmcbkbbMay/uU+Gora0NcoooKDmcaOrD1+svaJpGKNaGpz++3Xic+j/QuVvWEIY6yRvurCGAxWIBJAkUKCIc9ZCamho88MADkCQJ33zzDZYsWYKbb74Z77zzDiZPngyWZbFu3Tp8/fXXuPXWW+FyuTBmzBisX78e69evx8yZM42xPB6PUQ7WEcXFxZAkCRRF4fLLL8cJJ5zQ7jldlej2Bo/H0+H3urW1FaNGjUpblink6o/NNGfRqaysxNy5c/Gb3/wGPp8Pc+bMMT1GIUBK1QgEAoFQ0OgXECHejzDvb3cXqDsMl0xGVzVgYOR6MAwDKeOCWTbZVY2i25eq6Y8tFgtESYAoi8bjzkh1b9G0JhwdaN29PB53muPIYXeYEvF04SgWSv79YiEWVUOq2z1XF47YhnpQggC2rRVcQz24eByuLpwohPbYbDYgRYCjRBFWe2EEY2eSOvntaiKsC5aRjMwtt9sNh8OByy67DGeffTZmzJjR7WtSFIVhw4blvXTPDLpLKpZxHI/JsqlueW1tfiMYW8dlKUJbm7/X29gZLa2tEOxJZ5nsHBylahRFwWn14Krj7jUeA+nCUWeOo+QYGY8T/3d2ruE4DpAkQJZIqVovYVkWhx12GH72s58hEomgubkZNpsN48ePx1//+lc0NzdjypQpAICpU6figw8+wN69e418IwA44ogj0NbWBpZlMXHixHb/OI6D3W7HIYccgp07d3b4HL1rmn78E0xklunrdbTOoYceio8//jjt2m/jxo3Yv3+/8b50Vq9enfb4X//6F2w2G0aPHm1qW3RmzpyJO++8E8888wz+9Kc/9WiMfEMcRwQCgUAoaHThKBwPIMwHTN+JSoodyWV62Zooilndjc8nLMu2E45ExZzjSFEU+KPN2Nv6PSiKwtCiMWiLNALQM44EU44jmgEoJrnsQLpYd7ldaG5MZhy5XNm7jQDtbiXLMogHk5dgQtiGIePbtxfWhYGij/8FcUgVPJ99CtuunVBLy0y5nAiacKQKgua0oyhQggCbtTBFklRRuCuB2Ahr5zUhU3ccud1uUBSFCy+8sA+3Mv/oZXyRjI5wEUnCUBOOo0DAD9dQX9oyt9UHf1ttr7exM1oyHEeKw4FgINDFGgMDmqZB0zQqvTXG446ek0s4joOacLAQx5F5vv/+ezz55JM4+eSTUV1djUgkgmXLlmHIkCGortZuaEydOhWvvPIKxowZY2T6TZ06Fa+99hoYhkkLoT7qqKNw3HHH4aabbsIll1yCsWPHIhaLYceOHaitrcUdd9wBALjhhhtw3XXX4Y477sApp5wCt9uNxsZGfPHFFzjrrLMwbdo0lJSUwO124/3330dVVRUsFgvGjBnT7d95xIgRkGUZK1aswOTJk+F0OlFTU4PLL78c8+bNw4IFC/DTn/4UwWAQixcvxsiRI3HKKaekjVFXV4d77rkHp556Kr777ju8/PLLuPDCC9MyDT/99FPEYjEjk+mTTz6Bw+HAyJEj2zmYAOC0006DIAj43e9+B4vFgvPOO68Hf7H8QYQjAoFAIBQ0DMPA6XAhzAcQjYdNd7PoqFRND8oeCC2oWZZt11VNVs1lHMXjMXzZ8gG+3PUBGJrDYxf8Hf/Z9jYATSgSRN7IOOpqXP0OHkWrhoPrQBOO3G43aiUt4yAmhk3lGwHa57msvBSxoOYuUFUgGmCMO6yZz7Xa7WieMAlieQX8M08BJQioWPkXIhyZxG63aztblgGWBSWKsBeo44hKsVxQmfaLFPRjoe40igohUBTdZS7SYEJ//yEh/TgeEsSsvx+CIIAXeDgt6c93WD2oC4Vys6Ed0NbWBnl0suRFdjoh8jxisZgpt1ShQdM0VFUx3EH9JRxBUaBK2XUQ7A8agnsGzGuWlJSguLgYy5YtQ1NTE5xOJ6ZMmYK7777buEGlC0epzqKpU6eCoiiMGzeu3THngQcewLJly/DGG2+gvr4eLpcLo0aNSutaNnnyZDzzzDN45plncO+990IURZSXl+OII47A0KFDAWiflV//+tdYsmQJrr/+egiCgJUrV3Z4vkxlxowZmDVrFpYtW4a2tjZMnToVS5YswcSJE7Fo0SIsXrwYv/rVr2Cz2XDMMcfgxhtvbHcT8eqrr8a6devwq1/9CgzDYNasWbjmmmvSnvPwww9j//79xuP77rsPADB37twOhSMAOOecc8DzPB599FFYrVacffbZXb6XQoIIRwQCgUAoeFxOF1oj9VChmp6o62IHzaZnHKX+rpDpyHEkmXQcuVxujC89EieNP8+YiB4z+gx8V78GFosFiqKAF+MATIRjp7i2DiRcLpdRqhYTwnB5zYdUD60ehu31uwEAYpyGJKidXgi7PV40swxUiwWSpTihNEX7LANisKJPCmhBgMKyoEURjgKdoGdb/mmz2cAyrOE0igohuJyunE/MCxU9TN4f541lqqrCH4u3C5rvjGg0CgAI84E0R6aiKgiHw7neZGMbQ4EAlJQQdMWhTbz9fv+AFo4oioKqJruqdSR8dvf5bBfH181rGmKRLJsq4e4LvF4vrBYbXv7swby8vtViM31uKC4uxm9/+9sunzNjxgx8/vnnact8Ph8+++yzDp/PcRzmzp2LuXPndjnuxIkTO81C0jnxxBPbdTjrDpZlceutt+LWW29t97upU6di6dKl3Y5ht9tx55134s477+z0OatWrep2nI7WP//883H++ed3u26hQYQjAoFAIBQ8TpcTrRGty4ZZ4aijjKOBJHqwHXZVU0xdILMsC5fVh2HFY41leqaHLhTF+AiAroN0iXCklcfEBG1fRYUwit3m3R1VVdVY95UV4VYOwQZrYlnHwlGRz4fd0YjxmOJ5QFGynhgTNPTsHkp3IIoC7J7C7EyXKhx11kkK0CblLpcbMUETOCJ8EG6TpZMDGYvFArvNiifXbcC6Rs0FGBUlCLJsNFXoDp7XRKe3vnoGAAxH5o6mDYhE+0Y44nkekihCThOOtJ8DgQCGDBnSJ6/bH9A0DQUKVCjG40w6u+lhPLcTpagzwSnbTLD+oLy8HM+/8BwCeSo79Hq9KC8vz8trEwY/RDgiEAgEQsHjdDnR1NIEIL1VdTakih06A0n06Cwc24zjiKEZqGr6BFS/I6wLR9GY5qLp6sI7WfY3sPZhLnG5XIgJYaiqirgUhtM5zPQYI0aMgCxSWPumNkFkGKbTyWJpcRHY+mRrYiaiiUhEODKHIRxJ2ueVkSTYCzQIOlUs6ko4ArTPY2qXP7e7MMWwvuKcH/0Ym7/9Fqmx0odXD8NBBx2U1fr68eunRyzAsKKxhkPm4Kqj0bBld643FwAMUUFJDcdOdPjLbAk+0NAcR8lSNTOOI/25mbqRqrQP2E6lkIQjQBOPiHjTdyiK0uVxkWGYLkt8CT2HCEcEAoFAKHgcDjsCsR0AYNrGPyhK1TIuksw6jmiGhqLKacvUTOEo4TjqqlRNEAQwLAWKSu7PgbAPc4nT6YSsyBDkOGJSxLQDDgBOOeUUjB492rj49Xq9nXazKioqArdjp/GYTriPzHYXHAjwPN/u88QwTE5EMt1JpzuOGEkq2GD8VDG2u86PbrcbsbguHIXhKj5wHEcAcPnll+dkHAoUyt1DEYi3YG/r93BY3H3WMTKUyE5SUr7z+s+hPsxV6g/0UjW9a2dHYk9Xk3qKAuJBFqFmTQByl4oQYnSX6xWacEToW+677z787W9/6/T3ixcvxrRp03o8flVVVbuyPIIGEY4IBAKBUPDY7XbExLDxsxkGuuOoI+FINikcMQxjOIyMMRJCkiEcxbX9253jSDc66ftQL/U4UNBDQONiBHEx2qMgYoZhMHbs2O6fCM1ZxKSUqumOo8EmHEUiEVx88cUdfp5mz57da4Egs1SNFsWCbT0vy3KHP3eEy+VENKR9d2NSGCXuwfW56Gt0YWPFl4+h3DMM7296Bd/Vr8WPpvzcED9yjZ6dlCocgeMAlu2zXKX+gqZpqFB7EY5NYe83Xuz9xguKVnH8FXvRuE07xnbmsk09FxLhaPAzd+7cLvOBhg8f3o9bc2BBhCMCgUAgFDypzgCzkz19IkqlXKsOJMcRwzDtuqqZDcdmGAaK1LHjSN+3cV4Lie3KccTzPOjElcNAEt9yiV4qGRejiAsR06WTZikqKoIaiRht5JloBCzH9fnr9jfBYBA8z+MHR/wYE0ceZix/5s37sG/fvl6PbziOEg4eSpIKthtgqsuoO8eRy+1C2x4t3ycuhuF0Du3TbRts6ELDiNKDsKv5WwwvHo8hvlH4ZOsqWLi++XzogdyqJd3xRlmtxu8GKslStc5LiboSjliWQcWEVpSPTorlpSOiCNTbOj3npS43c14kDEyqqqq67apG6BuIcEQgEAiEgidVODJbXiKKIhhWs8DrDCThSAvH7q3jiIYspAtHsqI91ven7jjqalxtXybuJB/gwlFUCIMX4/0iHEFRQMdiUBwO0JEIPD7foMtw0AXeoyefgjHDJhnL//fNUTlxtSWFIz0ce7A4jlyIiVoWT0wM96h08kDG6/WisqISexq/w77gdmO5JIqYOvWwLtbsObFYDADA+NugsiyYSBiUIEDluEEiHCVL1To6TnV17GI5Fha7DHdp8rzCWbXzX2eiEClVIxD6ByIcEQgEAqHgSRWLzF4YauVV6ReqA0n0YBgmrauarGqX5GburNIdlKrpmUepjiOWYbu8G8zzvLHvDtRSNV0oCsSa0x73FXpJGh2JQHE4wEQiKBqEwdi6iNvUth9Dy0fCH25NfCY5hMO971Cku4t0xxEksWAdR6liUXeOI4fDgZiouTNiQnTQOdH6GovFghdefKFfXzMejwMAKv70MhovvBiezz6FbddOqGVlA/54SlFUolSt865qXQlHDMNAkdN/r6ra485uaqQuN3NDhUAgmKOrIlMCgUAgEAoCvUNJeXmFaaeFIAigMzSWgSQcZWYc6YHKpjOOlPQJqJKRcRTjtRKoruB5HhSTmBCwB7Zw9MJ/70173FfowdBMTBMHmGgEJVm2Gh9I6ALJM2/ehz312/DK35/Ab5+5ChzLoqmpsdfjtxeOBkapWneOI4fDgbgY0br8CZEeZW4R+hdBEACOQ8Oll0Msr4B/5ilouPRyKAw7IM5JXUHTdO8cRyxjdFHTUWQKFEVllXFEStUIhL6DyLIEAoFAKHjOOussnHDCCT3qgiSKYlowNgBQTPJ3hY7mOEoKR1Ii78jMBTLLMJAyHUeJUjW9XCcuxMB1I0YJggCaTReOBkK5Xy5xuVyYMeM4NDU2wWa3YeTIkX36el6vFwBAJ0pYuFhsUDqOdLHk+p/ei+GVY3DJGfMR56P415q/wuXqfacwmqbBsCwoUQJUFWoBC0epraa7E46cTifiQhSCHIeiKsRxlEc2btyIVatWGcHQRx55JH74wx+2e54kSaAYBmJFpfbYognBKjvwhaNkxlHPwrFZlkVmp3VV0QSlzkg9FxLHEYHQd5BvF4FAIBAGBG53zyaP2kV6+jKK0sKyB8JFOsuyaeHYetmaKccRy4BX0x1HcsKBlFqq1l0ZIM/zoGhtIkvT2j480BxHNE3jjjtu77fXczgcYFjWEI6YWNQQkwYTukBSUzkGNqsDlVZNAGGZ3F2qshwHSpYARQFUtWCFIzNt4O12OxRVQSjWBqDvHXCEzlm9ejX++9//onhYHK17bait29OhcKQoClSKAtdQbywTKyqhonuhsNDRStWSzRfMOo4sFgtUub3jiOU6Pw6QUjUCoX8g3y4CgUAgDGo0cUhFqDkpirhLxQEjHOmOI1VVQVEU5C6yI7oeI0M4UtMdR7wQ67ZULc7HDccRALAcZeR1EPoGiqLg8njQlgjUpWIxeDyePG9V7tFdNlRGXSlN0VDkzjs0mYGzWEDJslGuNhiCdHWhyJ/I3LLb7fncnAOatrZWFA+N45AfNmLXOg/atnUcVK6qKiBJqHj5Re0xw6BuwUJA7jrPaiBgOI5SHpvBYuEgdFCqxnUhHJGuagRC/1AQwtHOnTtx3333Ye3atbDb7TjzzDNxyy23ZNXtwu/34/HHH8eHH36IQCCAqqoqzJkzBxdeeGE/bDmBQCAQCh1ZliHywLpVQwAAFK3i+Cv2gqYHxt1d/UJYUVUwFAWpB44jlmWhqOkCj6LIoCjKcBzFhCgcrq4dGPF4zOiqBgAMe+CVquUDt9sDOhYDZBkqzw9K4Uj/LtJUuiBK03S7YPeewrIsKEkClXitQhWOUkXh7ibeunAUjLemPSb0P23+VrC2RHacXUE4HIGiKB2L/CyLhgsvTl+WQ3ddvjC6qnXhOOrqpoeFsyIudSQcdf5dLTTHUWNjIwKB3gf69wSv12tkQg4GQqEQVqxYgZkzZ2LUqFH53pwDnrx/u4LBIC677DJUVVXhySefRGtrKx544AH4/X48+uijXa4biURw6aWXwmq14vbbb0dJSQl27949IO4gEwgEAqF/kGUZVoeCg3+YHrBLDTDhSFJVMIBRtmbmzqrmOEp/r7IqgWFY40I7zkfh9nY96YzHY2l5UQyrHnClavnA63GDjsdAxzXXUU/LNgcmFGCidKsrOI7THEcJZ0ehlqpNmzYNs2fPBgAcfvjhXT5Xv8kajLWmPSb0P6FQCJwn0bzAokBRVMRisXaB5SzLQg/ykYqKwUTC4BrqQanKgHfM0PT/s3fn4U1U3R/AvzOTvW3SvbRlBy27lB0KgqIgCoigvqIsslR2EQVRRBYBWUU2AQFREBREBV9xF1/EHyoKoiDiBiJLoaWlbbplm5nfH+lMkyZtkzRt0vZ8noeHZjKZubmdZmZOzj2XLZ5VrewaR+UFQ9VqDYTCUoEjG1Pu32owFcfOyMjA2LHjYDYHJhNXrdZg+/bXak3wKC8vD9u2bUPTpk0pcBQEAh442rNnD4xGIw4cOIDI4llCOI7DzJkzMWnSJDRr1qzM17766qswmUzYt2+ffKLs2rVrtbSbEEJIzcDzPFiFfXiaI5ZlnIrQBivHjCPH/70qjq1QyLOoSXiBh4Lj5Itui9VU7nAAwF7PiAstuYlnKXBULQxhYeDSroItst+MhIa6HwJTO4n2omR+oFQqAZ63/0PwZhyFh4dj5MiRHq0rDU2TMo5oqFrgmIqKoIuyn1M4lf3/MgNHVivi3nwDGQ89Av3330Jz4R/w9eKD9pj0lJxxBN+CvWq1GrzRNeOovIkxgqk4dm5uLsxmEx4b+hzioxtV676vZv6LLe8vQW5ubq0JHNVUJpOpVgbxAx44OnLkCLp37y4HjQCgf//+mDNnDr7++utyA0fvvfceRo0aVSt/MYQQQvxDFEUwbi5iGQY1KnAkFcWWZljzpsaRvcC2a3FsluXkaY55nq/wottsNkMdUdJnLCdQ4KgahIaGQmE2gy3+Frs2ZhxJWQhiqWFpUm0vf1DIGUf2wFGgbzL9QboGzjflOD0m1c9isSK0OCNTysx0N5RXpVIBgoD0EaNhi4xCTt87wVgsiP3ko6DNgvOUJ7OqlUet1kCwOb+GtzFQq8s+roMp40gSH90IjRNuDnQzPHb+/HmsW7cOZ86cgdlsRlxcHAYPHow2bdpg4sSJeP/995GYmAgAmDVrFo4cOYJdu3bhpptuAgDMnz8fWVlZ2LBhAwD7cb9t2zZ89tlnyMrKQmJiIsaOHetSLP706dPYtGkTzpw5A47jkJKSghkzZiAyMhJpaWm47777AABz5pRMSLF//34kJCSU+37S09Oxdu1a/PTTTygoKEBUVBR69+6NGTNmAAC2bt2K3bt3Y+PGjVi5ciX+/vtvJCQkYNq0aejZs6e8nf/7v//D3r178ddff8FsNqNJkyZITU1F9+7d5XUOHjyIRYsWYdu2bdiyZQtOnTqFe+65B08//TT++9//4q233kJaWhrUajUaN26MGTNmoFWrVgDs57fdu3fjwIEDuHbtGmJiYvDggw9i+PDhPv0eq1rAz5jnzp3DsGHDnJapVCo0bNgQ586dK/N1ly5dQmZmJvR6PSZMmICjR48iJCQEd999N2bPnk0nTkIIIQCKC5G6ue9kGO9mLwoUOXBUfEPt61A114wjmzzFMccpPA4caTnHjCOeimNXg9DQUHAWC9jiIF1trGMj3WCW/psURAGclzefZVHV4sBRnjnH6TGpflarDZYiFryVgbWweIixzbXgtfQ7skZFA0olbCr7l+cMbys3s6YmsM+qJkKEb7OqaTQaiLzzuU2wMdCElH1cOwangiVwVNPMnDkTERERmDt3LkJCQnD58mVkZGSgdevWUKvV+Omnn5CYmAhRFPHLL79ArVbj5MmTcuDo559/xqBBg+TtzZkzB7/88gvGjx+Pxo0b49tvv8X8+fMRFhaGHj16ALAHjSZNmoQePXpgyZIlKCoqwubNmzFz5kxs374d0dHRWL58OWbPno1JkyahY8eOAIDo6OgK38/ChQuRmZmJp556CpGRkUhPT8fZs2ed1rHZbHjuuefw8MMPIyEhAe+//z6efvppvPnmm3LiytWrV9GzZ0888sgjYBgG3333HWbMmIFXXnlFbo9k3rx5GDJkCEaPHi33z5IlS/DII4+gR48eMJlM+O2335CXlye/ZvXq1fjggw8wZswYtG7dGqdOncKGDRugVqsxdOhQH36TVSvgZ0yj0ei2yKNery+3sFhmpn32iBUrVuCuu+7C1q1b8ffff2P16tWwWq1YvHixz20K5g8dqW3B3MaagPrRP6gf/YP60T/K6sfyLlKlbJtgJt3cFiccyYEjpVLpcdtVKhVsgvNQPV6w1zjiOA4KjoMFgEKpKPd4tJgtTsWxGY63D18L8j4MBH/+XYeEhICxmMFY7IGjsLCwWtfn0hCdi9f+Rl5hLhiGQaP4m1BQZIQgiH55v0qFAhBKhqqp1eoa349SELHAlAuFQllmxgqdZ/yjvH60Wq1I+02P2KZFuHzGnhXo7hwjDV1jLRYIDkPTGIsFISEhNfp3xHFccXHski84Sr+f8vrQXeCIt7HQacvuF8fAkTfnRWKXk5ODK1euYMaMGejVqxcA59pqrVq1wsmTJzFo0CCcP38e+fn5uPfee/HTTz/hwQcfRFpaGq5du4bk5GQAwPHjx/HNN99g7dq16NatGwB7KZnr169j69atcuDolVdeQcuWLbF8+XL5Oq1Zs2Z4+OGHcfToUaSkpODmm+1ZWw0aNEDbtm09fk+//fYbJk+ejDvvvFNedvfddzutY7VaMWbMGAwePBgA0K1bNwwbNgxvvPEGFi1aBAB44IEH5PUFQUDHjh1x/vx5HDhwwCVwNHToUKchxrt27YJer8fjjz8uL3PMZrp8+TL27duH2bNny5lVXbp0QVFREbZt24YhQ4Z4nbFX1QIeOCpLRanJ0vCCZs2aYenSpQCA7t27w2azYcWKFZg+fTpiYmK83i/LsoiIiPCt0dWoNs6oEgjUj/5B/egf1I/+Ubof1Wo1BJ5BXqYSOoMN5kIOvNVeh0Gn0wX9Z77BYABQMkRNGrIWHh7ucdtDQkIguCmOrVarERERYb9pL7JftEv9V7ofRVGE2WwFp3Qsji3AxtuCvg8DyR9/19HR0YDZDLZ42EtCQkLQXVBWVnh4OABg3Z7nAAAKTomtz3+B85fPQuCsfulHnVYLJi8fTPHfUlRUVI0/dkVRBMuwyDPnQKPWVPh+6DzjH+76UalUIi4pG6FRFtRvY8TZ/8XAYDC4/E7q1asHAGAtZghS/SNRhGgyISYmpkYfkzqdrjjjyH6eiIiIcHk/ZZ1jAPv5zmphnc/XFg56vd6jfomJianxdaKqm8FgQHx8PDZu3Aij0YhOnTohLi5Ofr59+/b49NNPAQAnT55EUlISUlJS5CSNn3/+GUqlEm3atAEAHDt2DHq9Hp06dXLKuOvUqRNWrVpln+nWasWpU6cwbdo0p0lKGjZsiOjoaJw9exYpKSk+v6ekpCTs3r0bHMehS5cuaNCggdv1+vTpI//McRx69eqF//u//5OXpaenY/Pmzfjxxx+RmZkpB0RbtGjhsq3S7W3RogWMRiNeeOEF9O/fH7fccotTRugPP/wAALj99tud+qlz587YtWsX0tPTER8f7/2br0IBDxzp9XoYjUaX5Xl5eeXWN5IuMKRIpqRbt24QBAHnzp3zKXAkCILb9gQLjuPkPqsJswEFK+pH/6B+9A/qR/8oqx+tVivMhQx+OhCP9gPT8e9JPbKvaKHS2odeZWdnB7DVFSssLAQASJVfpOLYhYWFHrfdZrO5yTjiwbIssrOzwbL2b2hFUZQzgd31oyAITrOqsQoReXm5Qd+HgeDvv2vRYgFjNkOl0QRsqueqJA15nHj/fMRFJspfHtaPawoTe8Mv/cgwDBhBkIeqefM3FMxUajUKzEaEhGnLfD90nvGP8vqRYRmodDw4pQh1iP0TOy8vz+V3It18MqaS+nCMzQbwJZ/JNZXJZLIHwYqHVhuNRpfhk0ajEeHh4e77kGFgKYLT+dqUp/C4X4xGo1+C6nq9vs5kLjEMg7Vr12Lz5s1YuXIlioqKkJSUhBkzZiA5ORkdOnTA66+/jvT0dJw8eRLt27dH+/btkZubi3/++QcnT55Eq1at5GGWubm5MBqNZQZ+MjMzwTAMeJ7HmjVrsGbNGpd10tPTK/WelixZgk2bNmHz5s1YsWIFGjVqhEmTJuG2226T11EoFC7By8jISGRlZQGwxwRmzZqF/Px8PPbYY6hfvz60Wi1effVVt+0rHdjs1KkTFixYgL1792L69OlQqVS4/fbbMWPGDBgMBuTm5kIURfTr18/te6DAkRvNmjVzqWVksVhw8eJFl9pHjho0aOA2ouxrMTZHNeGEyvN8jWhnsKN+9A/qR/+gfvSP0v3IsiyUahGt77wKncGG5j2ywVtzcPrTeLAsG/R9Lt1A24qzJASHQt+etp3jOPClM44Em1wUm+PYkvWKt1m6H6UAFqcoKV7MKUSYTKag78NA8sfftTT8iCsshFqjqZX9LV23xUbEOxWV5TgldFqdX/qRY1n7MDWHovi1oS/VKjVyTTmIVIdX+H7oPOMf7vqRZRlAlIq8o/h/0WU9aXghayqSlzHFgVOdTlfjfz+CQ4F7QRBc3k9Z5xjAniHMMECHISXn69++iLfPtuZBv7jrb1KxRo0aYenSpbDZbDh16hQ2bdqEp556CgcPHkS7du2gUCjw008/4ZdffsHTTz+N0NBQNG/eHCdPnsTJkyfRt29feVtSdtjLL7/sdl+RkZGw2WxgGAaPPvooevfu7bKOlGntq+joaDz//PMQBAG///47tm/fjueeew779u2Ti3zbbDaXkjk3btxAVFQUAPtQsj/++AMrVqxwamNZE4K4Gyk1YMAADBgwADk5OThy5AhefvllKBQKzJ07F3q9HgzDYMuWLW5jGg0bNqxUH1SFgAeObr31VmzatAnZ2dlypO6LL76AxWJxeyBJVCoVUlJS8N133zkt/+6776BQKNC8efMqbTchhJCaQaGwf1sZFm3PuNEZilOCheCvbwSU1IGQMo2koWreFsfmXTKOrFBwiuLn7f+X96WLdLHEKp0zjmhWtaonfWPPFhaUO7tQTSYFx2y883Fqs1mg81PBYIVCAdYh46g2FMcGSvqups/IVdPZZxQrfiCWLCtNmhWRdZhYgCsOItX0GRPtfSDKwSNvv8jXarXgrXA6X4s8Q0Xfq4lCoUCHDh0watQozJw5E5mZmWjYsCGSkpLw3//+F5mZmWjfvj0AIDk5GV988QUuXbok1zcC7EOt3nzzTSgUCrl4dmlKpRJt27bFP//8g4kTJ5bZHimg4m52Qk+wLItWrVph4sSJ+Oabb3Dp0iU5cAQAhw8flmsc8TyPb775Rh5yJ2XBOgZ1rl69ilOnTnkd1AkPD8fgwYNx9OhRXLhwAYC9nwB7hpZUWyrYBfyM+dBDD2HXrl2YPHkyJk+ejKysLCxbtgyDBg1yGqo2Z84cHDhwAL/99pu8bMqUKXj44Yfx9NNPY/Dgwfj777+xfv16PPLII4iMjAzE2yGEEBJklEolBN714p3nxRpRC0G68JYCR4IPmbUKhcJtxpFCWRwwcsg4KoscOCo1VK3ARIGjqiYNAWCLiqDW1OxZl8oiBT0sVufjyWIzI0Ltn7o8HMfZ6xsVZxzVhMCxJ9Qq+zGhqeEzctVG7gJHGo0GnFIJrqhQXsYW/1zZTItAs7/fkuLY5dWrdUej0UAQ7DXsi0dQw2ZloNVq/dzSqnU1898as8+//voL69atwx133IHExEQUFBRgx44diI+Pl4MsycnJ2LVrF5o3by5n6CQnJ2Pv3r3gOM6pcHXXrl3Rq1cvPPHEExgxYgRuuukmFBUV4fz587h8+TKee85ex27atGmYMmUKnnvuOdx5550ICwtDRkYGfvjhBwwcOBAdO3ZEVFQUwsLC8PnnnyMhIQEqlQrNmzcv99otPz8fjz/+OAYMGIBGjRrBZrPhnXfeQVhYmFNtIqVSiddffx0WiwUJCQl47733kJGRgdGjRwMAGjdujNjYWLzyyisQBAFFRUXYunWrx6VwtmzZgtzcXHTo0AGRkZH4+++/8f3332P48OEA7BlF999/PxYsWIARI0agdevWsNlsuHjxIk6cOIGVK1d68VusHgEPHOn1euzYsQOLFy/GtGnToNFoMHDgQMycOdNpPXepju3atcOrr76Kl156CRMnTkR4eDhGjBiB6dOnV+dbIIQQEsRUKhVKxUwgivYL05rwDb2/AkelMzl4wQaFqjjjqPgK3ZOMI8dZ1TiFCIuFAkdVzTFwpAkLDXBrqob0HksHjqw2i9+mKJczjooDR7Um40ht/xxTqoP/86w2EwURcpyk+H/BYVikhGEYhIaFIbvQIXBUaM84qumBI5Zli89RZQeORFF0WSaRAkS8lQXL2fvOZhFrTODIYDBArdZgy/tLArJ/tVrj9TEUFRWFyMhI7NixA9evX0dISAjat2+PBQsWyMF1KXDkmFmUnJwMhmFw8803yzMFSpYuXYodO3bgvffew7Vr1xAaGoqmTZti4MCB8jrt2rXDli1bsGXLFixatAhWqxWxsbHo3Lkz6tevD8B+PM2dOxebNm3C1KlTYbFYsH//fiQkJJT5fqTg0r59+3Dt2jWo1Wq0bNkS69atk2skA/bP/0WLFmHlypU4d+4cEhISsGzZMjlLSqVSYfny5Vi5ciXmzJmD2NhYjB07FsePH8fZs2cr7NdWrVphz549OHToEAoKChAbG4sRI0ZgzJgx8jpPPfUUGjVqhP379+O1116DVqtFw4YNcccdd1S4/UAIijNmkyZN8Nprr5W7zrJly7Bs2TKX5SkpKZWquk4IIaR2U6lU4G3OF69SBlJNChzxfs44sjlmHLEVZxxJaduOgSOWE2CxWMt6CfET6ThlTSaoo2pnRnVJ4MjktNxsLfLbTWNtzTiSjg91Dfg8q814QQDDFgdMiv8vq96OwWDAFceMo8ICcAqFXP+oJhNFoVIZRwDAWxkoNfYveAQBNSZwFBsbi+3bXwvYBAYGgwGxsbFevSYyMhILFy4sd52ePXvi2LFjTsvCw8Px/fffu11fqVRi/PjxGD9+fLnbbdmyZZm1kCR9+vRxmv2sIiqVCnPmzPFo3TZt2mDHjh1lPt+qVSu8/vrrTsvuvvtup8cDBw50CohJevbsiZ49e5a7f4Zh8MADD+CBBx7wqL2B5lPgaMuWLUhPT8fzzz/v8tyiRYuQkJCAcePGVbpxhBBCSGVpNBrYSsU2eCsjPxfsSmcc8T4HjmwQRVG+kBdEGxSK4kyj4qFq5W1TqjHAlso4slqct0v8Tw4cmc21NjggBY7MpQJHFqvJb3+nHMeBEUsyjmpb4EhZS4+NmsJqscqfj9KQ3rJqwEVGRIDNy5cfc4WFCC0ulluTlZyveKfHnpICZ7yVBcAX/19zAkeAPXjkbfCGkJrAp6nHDhw4UGaxqxYtWuDAgQOVaRMhhBDiNxqNBrxVhGN2vJSBVBMuRkuKY6P4f++LY0tDcqSLeQDgBR4Khb1OAOdBxpHbGkfFP/tauJJ4RqrnwFhqb+CI4ziolCqYLaUyjiwmv/2dKhQKMIJY+zKO1PbjQ6UK/ppttZXVaoXNxssZmYriSQSKiorcrh9hMEDl8BxbVFTjh6kBcPhiQnB67Ck546j4HC19yVMTztWkegiCAJvNVua/8oZCksrxKeMoLS0NjRs3dvtcw4YNcfny5cq0iRBCCPGbkm8wGShUxVk7lprzLab0ja10MSRdE3mbcQTY6xpxrP1nm2CVl0vbKu8iXw4cKUpqdkjfrlss/qtDQ1xJgSPWZgua4ZU//fQTrl27huTkZMTHx/tlmxqNFiZLodMyk6XIb8N3OI4DpBpHTM2YVdET0vERLMdGXVRQUAAAUKiLg5Iq+//5+flu1w8PD4ei1FC1SIf6KzVVyfmq7MCRu7pPEulv3VZ8jrbVwIwjUrUWL16Mjz76qMznN27ciI4dO5a7jdTUVKSmpvq7abWeT4EjhUKBGzduuH0uKyurxqdZEkIIqT0cL0QVKnvGjXQxWrqgYzCSzqmVrXEE2OsaSbeW9qFq9m93GdbzoWpcqVnVAHtQqaZPIx3MHIs4B0uwY/78+bDZbOjbt6/LhCa+0mq1MFlKsjBEUYTZ7N/AEVM8ZZO3Q2iCmRQ4qgmzRNZWUk0bZXHgSPrfaDS6Xd9gMIBxKI6tLCpCeGLZBX9ritIZR+7+zsrLCHH8osfx/9pQ+4n4x/jx48utCdSwYcNqbE3d4lPgqE2bNnjnnXdcikMBwDvvvIM2bdpUumGEEEKIP4SG2mehsmcZFQeOzDUncFQyVK04cATfA0eCYJOX8YLNJePIk8AR4xC3oKFq1cMxIBAMM4HxPA+bzX4sSUXT/UGr08JkLrmZttos4AXezxlHPBhBABskATh/kI6PYDg26io5cKS1n2MYFlBrGeTk5LhdX6/XQzSZ7MMmWRZKU+0YqiZP5lB8rvG9ODbr9D9lHBFJQkJCubOqkarj0xlm7NixmDBhAkaOHInhw4cjLi4O6enpePvtt3H8+HFs2bLF3+0khBBCfCIFh6yWkqAIb7FfzEpBpWAmp/4XPxZ8GKomBZ9sjoEjkZdvNJniuaPLu8i3WCzgFIDjKlLgyGqlmdWqkmOWUTAEBxx/3/4MGupKZRxJQSR/BXgdZ1ULlswtf6CMo8CTRmKotCV15FQ6AdnZ2W7XNxgMgCiCLSqCEBICptbVOOKdHntKqVRCoeBcMo4ocERI4Pl09XHrrbfihRdewPLly/Hkk0+CYRiIooiwsDAsWrQIvXr18nc7CSGEEJ9IQ6ikLCMAsJo5KJWKGlETpPSsapUZqsY7BY4cM44qDhxZrVYwLJCXqYTOYIO5kIMpz37zXdbMQcQ/HINFwRA4koJFCpaF1Z+BoxAdTPkF8mOp3pFfM45EEYwg1qqMI+mYCIZjo67KysqCQllSRw8AFBozsrKy3K6v1+sBAKypCIJOB7GwUF5Wk3kyVK28GkcAoNFq5OHklHFESPDw+QzzwAMP4J577sHJkydx48YNREZGIjk5mcagEkIICSpSVpFj4MhmZhESWjPOVyUX4n4IHInuh6pJaUQVDVUTBeCnA/FoPzAd/57UI/uK/WKeMo6qlmN2TDDU5pEChaFKBcxm/w1V0+l0uJ6VJz8uqoKMI/A8INaujCPpvVDgKHBu3LgBdYhz7R6VzobMrAy360tfaLBFRWDMZqD4C/iazpOhahXNeqXVauRMI5uFgVKpoGObkCBQqb9CnU6HlJQUf7WFEEII8TulUgmNRg2ryTHjiK0xF+mlM458mWpWrpMklAyjEAReXs4yFWcc2Ww2qNRAy4FXoTPY0LxHNgpz8nHmixi53g2pGgzDyNndwRDwkDKOwlQqWMx+zDjS6WCypMuPi8z27CO/Bo6Kh6oFQwDOXyjjKPCuXr0KRmFFXqZ9uGBYtBUsJyAzI9Pt+nLgyGwCW1wnrKack8rjScZRRbRabUnGkY2FRkszdhISDHw6w/z4448VrtO5c2dfNk0IIYT4XWhYCKyOQ9VMLKLCIwLYIs+VTG9sfywl+XsTQKhoqBo8qENhtVrBKuw3RACgM9ioxlE1YjkOvM0WZIEjJYx+zjiShqcBVVPjCIIARhDA1aIgi/R3HAzHRl11/vw55Gcq8dOBeDCsiFvHXkJephqFObkQRdElKC9lwrImM9jiv6GaMFlDRUq+6OCdHjuq6MsPnS4E2Q41jqSC2YSQwPLprDly5MgKi52dPXvWpwYRQggh/mYwhKPAVHJTZTMrYEgID1yDvOCPGkfSDWXpoWrScsaDjCOr1SoHikraZn9MGUdVL5gCR9JManqVCtf9WN9Kp9OhyFQSOHIcquaP2dsUCoU9cMTz4NjA96O/UMZR4CmUCkQ3LkDD9kZ5WWyzApzPVMNsNrsEP1QqFTiFAozZDFYa+lkDJmuoiHQO4YWyi2NXGDjS6pB5o6TGEdU3qrvy8vKwZ88e9O3bF02bNg10czyycuVKHD16FAcOHAh0U/zOpzPMzp07XZZlZ2fj0KFD+OmnnzBv3rxKN4wQQgjxl/DwCORcd5hVzaxEeHh44BrkBTlwhJLAkTR0yVMlGUclQ9V4h6Fq0pbK2ybP82BY5wt+hjKOqg3HcbAiOIIDUhDHoFbBnFdQwdqe02q1pTKOCsCyLDQajV8CRxzHQZRnVas9Q9Wkv+NgCCrWVWZTEXT1bXJGJgDowu0B9fz8fLdZMxqdDqzFDMZiDxzVhjqxjhlH3s6oJtHpQsCnF3/ZYWUQpq1Z/ZKRkYHc3NyA7NtgMCA2NjYg+64KeXl52LZtG5o2bVpjAke1mU9XH126dHG7vH///pg3bx6++eYb3HrrrZVqGCGEEOIv4YZw8JdLZlCzmLgaM4NN6eLYoijC28txKdjgVONI5OXlnlzg22w2l8CRlPTE87ybVxB/En3INKsqUhAnXK1GkR8COhKdTgeTuQhCcQ2iInMhtFqdzzegpbEsC/A8GIslKAJw/iIdExQ4ChyTyYwwhfPnI1f8uKygp1arA2s2gy2uE1YbAkclGUe2Mj+rKppVTavVQrRJgSMWurCa0y8ZGRkYO3acXycN8IZarcH27a/VquCRv5jNZqjVVC+rMvx+1rzzzjvxzDPP4LnnnvP3pgkhhBCfGAwG2Ez2U54oApYiEQaDIcCt8oxLjSPR++CBPNNNGUPV4MFQNbcZR8WPKXBU9eLq1cPFf/5BdHR0oJuCoqIiAECERg2rzQae5/0StJCGpJgtRdBqQmCyFELnx2Eq0o156OlfoGvRwm/bDTTp7zsYgop1lc3m+vkoDe0tayivTqcFY7GAsVjAchxUKpXb9WoSx8AR4/VXHHYajQYCXzyhg42FTltzaj/l5ubCbDZh+oR5qJ/QuFr3fTntAta++gJyc3O9DhydP38e69atw5kzZ2A2mxEXF4fBgwejTZs2mDhxIt5//30kJiYCAGbNmoUjR45g165duOmmmwAA8+fPR1ZWFjZs2ADAXgdv27Zt+Oyzz5CVlYXExESMHTsW/fv3d9rv6dOnsWnTJpw5cwYcxyElJQUzZsxAZGQk0tLScN999wEA5syZI79m//79SEhIKPf9bN26Fbt378Yrr7yC1atX488//8Rjjz2GkSNH4pVXXsHRo0eRlpaG0NBQtG/fHk888YTTuXXSpEnQarW45557sHnzZly/fh2tWrXCnDlzUL9+fXm969evY/ny5fjhhx+g1+vxn//8x217zp07h3Xr1uGXX34BwzDo2LEjpk+fjgYNGsjrdO3aFVOmTEFeXh7++9//wmazYfDgwXj88cdx/PhxrF27FpcvX0arVq0wf/58xMXFVfh79Te/B46MRqNcNJEQQggJBgaDAfk5gDFDBZuFhcCjxgSOXDKO4FpotSLuMo54wTHjqOJt8DwPQRDlWYMAIDTSPiyDahxVvQ1r18JisQRFAV2TyQSWYWBQ2290i4qK/FKfRQocmSyF9sBRccaRv/To0QOLFi2CzWZDo0aN/LbdQKPAUXArq6aPVqsFY7WAsVqgriUFoEuGqpU9c2FFNY60Wi1sluLznk1RI2sc1U9ojKaNkwLdDI/NnDkTERERmDt3LkJCQnD58mVkZGSgdevWUKvV+Omnn5CYmAhRFPHLL79ArVbj5MmTcuDo559/xqBBg+TtzZkzB7/88gvGjx+Pxo0b49tvv8X8+fMRFhaGHj16ALAHjSZNmoQePXpgyZIlKCoqwubNmzFz5kxs374d0dHRWL58OWbPno1JkyahY8eOAODxlydWqxXz5s3D8OHDMXnyZHnWwhs3bmD06NGIiYlBdnY23nrrLUycOBF79uxxykT9888/kZOTgylTpoDnebz88suYP38+XnvtNXmdWbNmISMjA7Nnz0ZoaCh27NiBjIwMp+2kp6djwoQJiI+Px7x58yAIArZu3YoJEyZg9+7diIgomahl37596NSpExYuXIhff/0VW7duhSAIOH78OB599FEolUq89NJLWLx4MdavX+/tr7nSfAocpaWluSyzWCz4448/8NJLL+GWW26pdMMIIYQQf5G+mTn533ouy4KdnHFU/NiXjKOS4tjOgSNvtsPzPCyFLH46EA/Anm3Ua8wle5sqGHpAKk+pVEKpVFa8YjUoLCyEVqmETqmQH/s3cGTPaDJbTdBq/XdDrVKp0KlTJ79tL1h4UtyeVC2FgoMoOPe/wNsfl/V3q9NowOTlg7Vaa2HgyLvziyONRgPeJgWOWJpVrYrl5OTgypUrmDFjBnr16gUATp+TrVq1wsmTJzFo0CCcP38e+fn5uPfee/HTTz/hwQcfRFpaGq5du4bk5GQAwPHjx/HNN99g7dq16NatGwB7Ns3169exdetWOXD0yiuvoGXLlli+fLn82dWsWTM8/PDDOHr0KFJSUnDzzTcDABo0aIC2bdt69b5sNhsmT56Mvn37Oi1//vnn5Z95nkfbtm0xaNAgHD9+XG4vYK9N9uabb8qBnfz8fLz44otIT09HXFwcvvvuO5w9exavvPKK3F/JyckYPHiwUw3Nt99+G1arFevWrZO31bp1a9x///149913kZqaKq8bExOD+fPnAwC6deuGb775Bnv37sXbb7+NJk2aALBnOa1atQp5eXlyMKy6+BQ4uv3228uskt+kSRMqjk0IISSo3HrrrWjatKmcGaNWqxEfHx/gVnmmdMaRIIpgvbxBlAJHTjWOHDKOSu/LHUEQoA61odUdGQ7rlzxH6g574EgBnaIkcOQPjkPVpP9rYrZBoFDGUeCoVCoU5nLgrQzMhfb/payZso5hnVYLLjsHvNUKVS2pveI4VA1lDFWrKONIo9HAVlxjnKfAUZUzGAyIj4/Hxo0bYTQa0alTJ6cv1tq3b49PP/0UAHDy5EkkJSUhJSUFixcvBmDPNlIqlWjTpg0A4NixY9Dr9ejUqZNTNnKnTp2watUq8DwPq9WKU6dOYdq0aU5D3Rs2bIjo6GicPXsWKSkplX5vUpDK0bfffovt27fj/PnzKCgomdzh0qVLToGjm266ySkbSArcZGRkIC4uDmfOnEFoaKhTkE1633///be87Oeff0anTp2cthUfH4927drh559/dmpb6TrSDRs2RFZWlrxvAPLwtoyMjJoROHrxxRddLi7VajUSExPRtm1bOnERQggJKgzDOI0lr0mkc+q1ggIU2WzIMZs9G1vmoCTjqOQiTnDIOPIkU0EQBLCc6DRrkL19FDiqawoKChDikHHkePFdGdINopRxZLIUQRtJgaOKSDfilHEUOIIoIv2vMMQnFeLfk3pkX9Hi5l5ZAFDmzZ1arQZrs4GxWqGpJYEjuZ5eOcWxK6LRaMBbRYiifVY1ChxVLYZhsHbtWmzevBkrV65EUVERkpKSMGPGDCQnJ6NDhw54/fXXkZ6ejpMnT6J9+/Zo3749cnNz8c8//+DkyZNo1aqVXHg6NzcXRqOxzMBPZmYmGIYBz/NYs2YN1qxZ47JOenp6pd+XRqNxCdr+9ttvmDlzJm699VaMGjUKERERYBgG48aNg9lsdlq39N+tlDkoleTJzMx0OztvZGSk0+O8vDw5c6r0ehcvXnRaVjpzV6lUVtiO6uRT4Gjo0KH+bgchhBBC3JAuvt86+zfaxUTju7R02Hjvago51p2Q/hchelXQWBRF9/EqhgJHdU1BQQF0Cg4hxRew/g4cmS2m4v+LEKmNKO8lxAEFjgKncaPGOK86htAoC5r3yAZvzcGNS1rodJoyi16rVCp74Mhmq3WBI7ESNY6kzwGBZ8DbQDNhVYNGjRph6dKlsNlsOHXqFDZt2oSnnnoKBw8eRLt27aBQKPDTTz/hl19+wdNPP43Q0FA0b94cJ0+exMmTJ52Gg+n1ekRERODll192u6/IyEj7LK0Mg0cffRS9e/d2WccfNSjdfR4ePnwYoaGhePHFF+Xj8+rVqz5tPzo6Gjk5OS7Lb9y44fRYr9e7LJPWqymz+0ooNYgQQggJYtLFzX9aNEfTcD261IuFSuFdrZuSoWr2AI9YHEAqHTgq78ZTFEWAcb3gp3vVuic/Px8hCg4hxRlH+fn5ftmudMNosdoDRxabiW4avUCBo8Bp0KAhjOka/PuzHtf+DMH1f3T492dDubX05Iwjmw3qWjCjGlByDNoEW5nHY0WBI+lvnrcysFlFyjiqRgqFAh06dMCoUaNQUFCAzMxMaDQaJCUl4b///S8yMzPRvn17APZ6Pl988QUuXbok1zcCgM6dOyM7OxsKhQItW7Z0+adUKqHVatG2bVv8888/bteRZk3zd3aN2WyGQqFwOjY/++wzn7bVqlUr5Ofn4/jx4/Iyo9Ho9BgAbrnlFvz444/Izc2Vl6Wnp+PUqVNyX9YUPs+qduHCBezduxfnzp2DyWRyeo5hGOzYsaPSjSOEEELqOukCJ0arhVahQJhK6VKbqCIlGUf2TCW+uNaRPFTNx2mTJRXdCJDaJT8vDzFKJdQcBwXL+i1wJN0wWqxm+X8KHFWMAkaB16lTJ/zw4zGYr5XM+BQTDfTqdWuZr1EqlWBsNjC8DWp15YvLBwN/DFUrCRyxEAXKOKpqf/31F9atW4c77rgDiYmJKCgowI4dOxAfH4/ExEQA9iDRrl270Lx5czlLJjk5GXv37gXHcU6Fq7t27YpevXrhiSeewIgRI3DTTTehqKgI58+fx+XLl/Hcc88BAKZNm4YpU6bgueeew5133omwsDBkZGTghx9+wMCBA9GxY0dERUUhLCwMn3/+ORISEqBSqdC8eXOfJ4ro0qUL9uzZg1WrVqFPnz44ffo0PvnkE5+21b17dyQlJWHevHmYOnUqQkND8cYbb7gMNxs+fDgOHjyIadOmYcyYMRAEAVu2bIFer8f999/v074DxafA0Z9//on//Oc/iI2NxcWLF5GUlITs7Gykp6cjPj6+xtaRIIQQQoIRyzBycWwR3t8oyhlH8lA13ml5pdBNa52TbzSiiVoJhmEQqlb5LXCkUCjAMmxJxpHVRNkGHqDAUeB16dLFpbBtRVQqFRibDWwtyjjyZKhaRaRAkdXEOj2uSS6nXagx+4yKikJkZCR27NiB69evIyQkBO3bt8eCBQvkawQpcOSYWZScnAyGYXDzzTcjJCTEaZtLly7Fjh078N577+HatWsIDQ1F06ZNMXDgQHmddu3aYcuWLdiyZQsWLVoEq9WK2NhYdO7cGfXr1wdgP57mzp2LTZs2YerUqbBYLNi/f7+ckeStlJQUTJ06Fe+88448DO+ll17CAw884PW2GIbBypUrsXz5cixbtgxhYWF48MEHkZGRgaNHj8rrxcXFYfPmzVi3bh0WLFgAlmXRsWNHPP74404Fs2sCnwJHq1evRs+ePfHyyy+jTZs2WLJkCVq3bo3Dhw9jzpw5eOKJJ/zcTEIIIaTuYhgGIhxmVfPygty1xpFzxlGl2lbpLZCaJi8/H2Gh9syKMJUKeXl5ftkuwzBQqVSw2OzDEqw2i8/fLNcllPFXM6lUKoC3geX5WnOcezJUrSJSTSirmXV6XBMYDAao1RqsffWFgOxfrdZ4XR8oMjISCxcuLHednj174tixY07LwsPD8f3337tdX6lUYvz48Rg/fny5223ZsmWZtZAkffr0QZ8+fcpdp7TU1FSnae4djRw5EiNHjnRaVvq9bdq0yW1bS68XFxeH1atXu6w7a9Ysp8fNmzfHunXrym1z6W0DcDtTfceOHd2uWx18Chz99ttvmD9/fsmFaHHNhD59+mDs2LFYvXo1du3a5b9WViezGRo3B0tpQlwcLKWKhKvefx+sB1XgbZ06wda5s/M+t2/3qHm2YcMAh+gke+4cVJ9/XvELlUqYSv3xKv/3P3C//17hS4WmTWHp399pmXrnTjAeFMO09u4NvlUr+TGTlQX1O+9U3F4A5pEjITqk+3G//ALlt99W+DoxIgLmhx5yWqb68EOwly/Lj1mGAXQ6qAsL5W/xAYBv1w7WUrMAeHI8AIDlnnsgNGxYso+LF6H66COPXmuaNMnpsfLoUXCnTlX4OqF+fVgGDXJapt6zB0x2doWvtfboAf6WW+THTH4+1G++6VF7zQ8+CDEqSn7M/vYblF99VeHrxJAQmEeNclqm+uwzsOfPV/havkULWG+7zWmZZts2wGot4xUlLP36QWjWrKS9165BtX9/ha8DANPYsYDDt12KH3+EotT4ZXe8+YwofTxW5jPCct99EOrVK9l2XfqM+Pln4ORJl7/r0jz5jChLoD4jGJaV35MgirglO9uzc1XxZwTDMGAYFoLII+bHtyEY0/DwtWu46cMPofn1V/Q9dQpJ166h/ddfQ20yAToduORk8A7p52qLBUP/ykQ85zz1eoO0q2jz+efQOBSYLP0Zwf32G5Rff11he2vTZ0RZ5xlJoK4jKvsZIYoijPn5CFMnQPXXOdx/+TL0hw5BU06BdG8+I4Zfu4akbz9C1LVrGHLhb0RnZjo9HyzXEWUJxGdEWzfXC6WvI8o6HoPlOqKmfUYwV69C8957Fb4OKPszosOpU7BevAg+Kwstjbkux0lN/Izgim/wedE+VM3xOuLha9cAAPpt24DoaKdj0fEzQsow6vVDAdpJ56mTJ8vcrbVUcWV/fEYwU6c63W95KjY2Ftu3v+ZUz6Y6GQwGxMbGBmTfpPbzKXBkNBphMBjAsiwUCgWMRqP8XJs2bfDKK6/4rYHVjRFFsB58cyaGuo5FZgoKPHot46bAlyevAwDwvPO2rFbP2usmWs+YzR69Vigqcn2th+/V5WQsCJ6/11IX2ozF4ll73b3XoiKn1zIMA9hs9uWOgaNSUzECXvxubDaXxx6/tjQPfzdiJX43Lsehh8c+AKDUDYKnx6Hb24pSv5syX+vmd8Pk57v9e3JZr/RxyPM+/248PQ69+YwofTzSZ4RvnxGwWACj0eXvujRPPiPKEqjPCI5lIBS/JVEEVB7u1/EzgmNZiKIAzpIPxmRECM9DZTKBzcuD1mJBCM9DbTaDycuzt7XUccgA0NkEaIqc/5JDbCXbkZUOItTBz4iyzjOSmnodYTKZYLPZEKZSgsk3I1wUIObllbsNbz4jwkQRSlMBuEIjNBYLVKWzFoLkOqIsgfiMUBb3r1PmUanP77KOx2C5jqiLnxFaqxU6qxWixQKdm991TfyMkLKMhOIaR47XESHF22Xz8wGVyulYdPyMkLKvlEWifF4qd/9Vca9RiZlCY2NjKXhThQRBKHcmV47jaPhuFfEpcBQXFydPP9eoUSP8+OOPSCn+duWPP/5wGedYk4gMAyEsrOL13LxHMSTEs9e6uSDx5HUAgFL1KESl0rPXukmBFdVqz16r1bq+NiTE/cm7ov2yrOfvtdQfvahS+f670WqdXit98yYqFM7fBLsZR+1xe0sXq1UoPH9taR7+bsSyfjceXAS5HIceHvsAgFLDWzw9Dt39blDqd1Pma938bsTQUIgefFMolj4OOc7n302ljsMyPiNKH4/0GeHbZwRUKkCvd/27dtM2l2UeHoeB+oywZwsVX2BDBM951k+OnxEsy0IQBfCqUNjUISjgOPDFx2SRSoUCjoNZrYYYFgbodPb+dG4EChUcTFrnv/98joO1dP+VHgJXBz8jyjzPSOvV0OsIaViaXqWCqFBAVGuQw/Plb8OLzwizSo1CpRK8Tg8jA0SX/r0GyXVEmQLwGWEp7l+x1PWMJ9c9wXIdURc/I/iQEBQwDGC1wqbTuWyvJn5GSKNRpKFqjtcRBVKtvdBQ13O1w3EoZRwVshwKOQ6iXl/+/qviXsMPw7hJ1Vi8eDE+KicTc+PGjejYsWM1tqjuYEQfBkY/++yziIiIwNNPP43Nmzdj/fr1GDJkCJRKJfbv34/Bgwdj0aJFVdHeKsfzPLI9SM8NFI7jEBERgezsbPClvhEgnqN+9A/qR/+gfvSP2tyPQ++7Dw/f1BhDbmqKN379Hd/m5mP7G97NXnrfkPtwV6tHcVvSMOQUXsf8/z6MRYsWoVOnTpg793mcOHEc//nPfzBu3Di3/bh48WL8duEQ2g3IcNruke0NMXnSFKeil6T2Ho9///03pk2bhtW39cBNEeHYfvosjuUV4rXX3/DL9sePT0Wbhj3w4J0TMe6F2zFjxgw8/PDDta4f/en999/H1q1b8eSTT+LOO+90u05tPR6rmz/78fLly9iydSt4nseDDzyAWxyG/dVU//77LyZOnIgm0a1hVuTgtde3yc9Nn/4Erl/PwM6dO1GvXr0y+9BoNOI///kPGrTLxaVTBmzatAmNGzcud78DBgwAAJ9nyCotIiKi3MkjTCYTzp07h5iYmBpVg6k2SEtLK3coYMOGDWt0Ekt1s1gsuH79Opo1a1bhZBQ+ZRxNmjQJGRn2C8fU1FRkZmbiww8/BGD/w509e7YvmyWEEEKIGxzLOg1VY3woSc0WD1UDIH/L601xbPtFtJv9ijSrU10iXbCHFd8s6VUqGPMqrrniKQWnAM/bwAv2YVu1pWhwdaAi2TVL/fr18UIFRYlrmpL6t7zL+WXt2jUAKp7NU/qbt1nsr1eUzsQjdVpCQoLPs6qRyvHpL7Fhw4ZoWFy8j+M4zJ07F3PnzvVrwwghhBBixzCM01A1lvM+jZ5hSgJH0v/eBHxYlgXEUmn9ov1fRTcCpPZwHKoGAHq1CvkFheB53i/HgULBgRds4Hl74IiOLc9R4IgEWsmsalawrG9fKEiBIt5qP89R8JiQ4EADOAkhhJAgx7CM06xqDONL4IiBCPs2pMBR6W+EywsksSwLUSj1vFjyHKkbjEYjFCwLrcIe0AlTOdc+qiyOU4AXeAjFxygFjiomBYwocEQCTToX8KJrxpGn5MCRzX6+ocARIcGBrvQIIYSQIGcfqibdHPoWqGFZVp6JRIBzxpEULyovcMRxnGvGkeDwHKkTjEYj9Bq1fKxImUeOM+xWBsuxxbPmFM/AREHJCkkBo/JmGiKkOkjnAmlWNV8wDAOOY+WMIxqqRkhwoLMxIYQQEuRYlpWSeyCKIjgfA0eAc2aCtzWORJehaoz8HKkbjEajHCwCSjKO/BY4YhgIouBTHa66jjKOSKCVDFXzPXAE2M8pUsYRBY4ICQ50NiaEEEKCHMOUZBzxougyhbBn22Dk4T/SUDVJTEwsACA6OrrM17sNHFHGUZ2Tl5eHUGXJjZxUJNtfQ9WY4iLuvtThqqukTCMKHJFAk4eqVTZwpOAgUOCIkKBCf4mEEEJIkGMYBtI9oSCKPl2Q22sc2Um1jqTtTJs2FY89llruVKwKhQIi73wTLwh0YV/X5OXlIcwhcBTq5xpHLMNAFEU5CEKBo4pRjSMSLPwWOHLIOKIvJggJDnSlRwghhAQ5pxpHAFgfLqTtwSfnzISSGkdMuUEjoDhw5JJxRIGjuibPaEQjh2K1SpaFVqlEfn6+f3ZQfEz6MpyyrpJrl1GNIxJg/gscsbBaGadt1hQZGRnIzc0NyL4NBgNiY2O9es3WrVuxe/duHD58GGlpabjvvvvw4osvom/fvlXUysA6ePAglEol+vfvX2X7uHTpEnbv3o1ff/0V58+fR6NGjfD222+7rDdkyBCkpKRg1qxZVdYWf6IrPUIIISTIsSwLwaE+kS81juxBItHNMs8oFAoU1yuWSfepFDiqO3JzcmDVKFFksyHbZEah1QadUuG/wBHxGmUckWAhZQfxog0c53vAxz6ZAwOWZWpU1mFGRgbGjH0UFrM1IPtXqZV4ffsbXgePJNHR0di2bRsaNmzo55YFj48++gharbZKA0fnz5/H0aNH0bp1a4iiWGuC+nSlRwghhAQ5hnWuceTrhbR0XykNVfM+cFQq44inoQR1TU5uLg5ft+CuJg2x9/e/cTIjEw0NehQUFAS6aXUWBY5IsHDOOPL9vMCxLESeqXHZRrm5ubCYrWjRJxO68OoNHhXmKPH74Wjk5ub6HDhSqVRo27atn1tW9/Tq1Qu9e/cGALzwwgs4e/ZsgFvkHxQ4IoQQQoIc6xA4EkTRt6FqcMg48uEGU6FQoFRNbbnGkdJh6BKp3Xiex9CbmqBpuB4T27dGodWGV0/95r/AkSgCTElQk4IhFaOhaiRYSH+3vGADw/qeKcRyHAQe4Liak23kSBduRVh0YLKOKsPdUDVBELBz504cOHAAmZmZiIuLw/3334/hw4fLr5OGu23duhUrVqzAH3/8gcTEREyfPh3dunVz2sfBgwfx9ttv4+LFizAYDLjnnnuQmpoqZy7n5eVh/fr1+Pbbb5Gbm4vw8HC0a9cOS5Ys8ej58kyaNAk//fQTAKBr164AgPHjxyM1NRUAsH//fuzZswdXrlxBZGQk7r77bowfP15u28GDB7Fo0SJs27YNmzZtwq+//orIyEiMHTsWgwcPlvfja8DTaDTiiSeegNVqxbp16xAREeHTdqoKBY4IIYSQIGefotz+s1j82GtMSaaRLxlHSqXSZaialHFEgaO6wWazwcbzaKAPg1ahgDbUfhmpUyhQVFTkl32IogiGtYc5pcekfJRxRIKF4w2zL0OqHbcjCgxYZc3KOKqN1q9fjz179mD06NFo3749fvjhB6xZswaFhYUYN26cvJ7NZsOCBQvw4IMPYty4cdixYweeeeYZfPDBBzAYDACAt956Cxs2bMBDDz2Exx9/HBcuXMDmzZshCAKmTJkCAFizZg2+++47TJkyBfHx8cjMzMR3330n76ei58sza9YsLFiwAGq1Go8//jgAyNlZ77zzDl566SUMGzYMM2bMwO+//46tW7ciMzMTc+fOddrO888/jyFDhmDUqFH4/PPPsWTJEsTExKB79+4+93NWVhYef/xxaLVabNy4EWFhYT5vq6pQ4IgQQggJcvYpyiuZcVQ8W1XpZZ5SKpXgeefXSwkOFDiqG6TgkFbhfPxpFRwKCwv9sg9RFMGAAcOw8mNSPgockWDhGDhiK1HjyH6+qln1jWqjnJwcvPPOO3j44YcxceJEAEC3bt1QUFCAN998E8OHD4dOpwMAWK1WTJ48GSkpKQCAxMRE3H///fj2228xYMAAFBQUYOvWrRgxYgQmT54MwJ71w3Ec1q1bhxEjRsBgMOC3335D//79cc8998jt6Nevn/xzRc+Xp2nTpggJCYFWq3UaksfzPF577TX07dsXTz/9tPw+AWDz5s0YM2YMEhMT5fUHDBiARx99VF7vypUreO2113wOHF27dg1Tp05FvXr1sHLlSmi1Wp+2U9UojEsIIYQEOZZlwTsGjnwpjo2SC3BfpjqXMo4c700FyjiqU0wmEwBAXSpwqeY4mEx+zDhiSm4YafhVxShwRIKFY727ytQnYh1m/CSB8+uvv8Jms+GOO+5wWt6vXz8UFRXhjz/+kJexLIsuXbrIjxs0aAClUomMjAwAwOnTp1FYWIi+ffvas1eL/3Xq1Almsxnnzp0DACQlJeHgwYPYtWuXvMxRRc/74t9//0VOTo7b9ymKIn755Ren5X369HF5fPbsWfB8qbRsD1y5cgUTJkxA48aNsXr16qANGgGUcUQIIYQEPZZlIYr2CxJ7xlH1f++jUqkAAKIAMMX3BhQ4qlssFgsA94Eji9nil30IggCGYSnjyAsUOCLBgmVZaLVaFBUVITQ01PcNUeAoKOTl5QEAoqKinJZLj41Go7xMrVa7XAsoFAr5vJGTkwMAGDVqlNt9paenAwBmzpwJvV6Pt956C+vXr0dcXBxGjx6NYcOGefS8L6T34cn7BOBSeygiIgI2mw05OTku26jImTNnYDQa8eSTT8rXWcGKAkeEEEJIkGNZFoLN/rMgQr6p9kolr7+lC0KBZ8ByxTeqxYGjYL/YIf4h3QCoSgWOVBwLS5GfAkfFGUcsZRx5jAJHJFgwDIOtW7ciJyfHaWiPL9ux/++vlhFf6PV6AMCNGzecZmrLyspyet6bbS1fvhxxcXEuzyckJAAAQkND8eSTT+LJJ5/E33//jb1792LFihVo2rQpkpOTK3y+su/TUVnvMzs726k/srOzoVAoEB4e7vW++/XrB47j8Pzzz+Oll15C586dvd5GdaGhaoQQQkiQKz2rGudDjSOgpCi2PLuaF6TgkJRl5PgzBY7qBpvNHr1UlJotScGy8nOVJfACWJaTg6MUOKoYBYxIMImKikKzZs2g0Wh83oYcOKrsNx6kUlq3bg2FQoFDhw45Lf/yyy+h1WrRokULj7fVrl07aDQaZGRkoGXLli7/pALajpo3b44nnngCAHDhwgWvn3fHMQtK0qhRI0RERODLL790Wv7ll1+CYRi0b9/eafnhw4ddHrdo0cLna7Mnn3wSd999N2bOnImTJ0/6tI3qQBlHhBBCSJArHThS+lLjqJJf3coZRzbXwBENVasbpPoNXKljiWMYvwWORFEAy7ByfRQKHHmOAkiktpADRywFjgIpPDwcDz74IHbv3g2lUol27drh+PHj2L9/P1JTU72qxxMaGorHHnsMGzZsQEZGBjp27AiGYZCWloYjR45g2bJl0Gg0SE1NRe/evdGsWTOwLIuPP/4YSqVSDt5U9HxFGjdujI8//hjffPMNoqOjER0djZiYGIwbNw6rVq1CREQEevbsiT/++ANbt27FwIED5WwoySeffAK1Wo0WLVrg888/x88//4zVq1fLz5tMJhw9ehQAcPXqVRQUFMjBtw4dOrgMdQOA2bNnw2Kx4Mknn8T69evRpk0bj/u2ulDgiBBCCAlyLMeVBI4QmLoPZWUcMQwDhYIuJ+oCKXDEljr+WIbxW4CH53mwLAuWtX9zS4EjQkhNU5hT/V+mVNU+p02bBr1ejw8++AA7duxAvXr1MH36dAwfPtzrbT3yyCOIiYnB22+/jXfeeQcKhQL169dHSkqKfB3Rrl07fPLJJ0hLSwPDMGjWrBlWrVqFJk2aePR8RUaOHInLly9j4cKFyMvLw/jx45GamooHHngAHMdhz5492L9/PyIjIzFixAiMHz/eZRuLFi3Cxo0bsX37dkRERODZZ5+VZ5MD7EPe5syZ4/Qa6fHGjRvRsWNHl20yDIPnnnsOZrMZTzzxBF555RUkJSV51rHVhK70CCGEkCDnr6FqlSEFjvKzlFCH8DAXcigyKqBUVn9bSGCVDlzaH/kn24UX7BlHNFTNc9IQD3dDPQipiWpqUWyDwQCVWonfD0cHZP8qtdLrz4HU1FSkpqYCsNcZOnbsmNPzLMtizJgxGDNmjEfbcFR6SBdgr+nTr1+/Mrc1bdo0TJs2zefnKxIbG4uXX37Z7XNDhw7F0KFDK9xGgwYNsGnTpjKfd9eP7hw4cMDpMcdxWLJkSYWvCxQKHBFCCCFBzp5xZP9ZECs3zbGvpMDR2f/FoP3AdPx7Uo/sK1qEhNAwtbqm9JAo+yP/3OgJgr3GEVscOPJleuO6ZtiwYbj99tvdDn8ghFSf2NhYvL79DeTm5gZk/waDwaloMyH+RIEjQgghJMhxjhlHEH0OHFWm0KgUOLqpZxZCoyxo3iMbl07ZYMqgLIe6QjruSpfSEf0YzBQFkWoceYlhGERGRga6GYQQ2INHFLwJDJ7ny631RsPqKycoeu+ff/7B4sWLceLECWi1Wtxzzz2YOXOmV9X4v/jiC0ydOhU33XQTDh48WIWtJYQQQqoXU2qomi836ZUtnCsFjjQhPDilCJ3BBqVahKimGdXqCum4u2DMg1UQwDBAs3ADjBazazSpHEajEQcOHHAqqM2yLAYOHAiLxQJjYQ5M5kIwfqydRAipOajQO/HFsGHDcPXq1TKf92T4WHkGDhyIgQMHVmobNVnAA0dGoxGjR49GQkIC1q1bhxs3bmDp0qXIycnBqlWrPNqGyWTC0qVLER0dmPGkhBBCSFVyrnHkj+wO7zOP3BbHtjHyclL7SbPnrfrxZwCAgmWxf8hdOJ15AybR82Pqxx9/xNtvv43I0DhwxUWwrxvTYDAYUFhYgKM/f4pbk+8Gy7AUOCKEEOKRVatWwWq1BroZtVbAA0d79uyRv3mS0mw5jsPMmTMxadIkNGvWrMJtvPrqq0hISED9+vXx66+/VnWTCSGEkGrFcRysfhiqVhlqtdq+f4fAEc8z0KrU1d4WEhhS4Ghacls0DddDql97c7gBfwmeB46MRiOUCjXmD9wlL1vw4XDk5eVBo9GgU8vb0bBec7AsR4EjQuogKeOIEo+IN5o3bx7oJtRq1X/lWcqRI0fQvXt3p7HZ/fv3h0qlwtdff13h6y9evIjXX38dc+fOrcpmEkIIIQHDsiyk22dfh6pVdtIrKWjA20plHKkpcFRXSNllMToNmkcY0CzcXt+KY1nodDqPt5OXl4cQdZjTMp1Kj/z8fABAhD4aGrUOHEeBI0LqNIocERI0Ah44OnfunEtWkUqlQsOGDXHu3LkKX79kyRLce++9aNGiRVU1kRBCCAkoe+DIf0PVfJnqmOM4cBzrPFSNZ6BRe16PkNRsUtaZmXcO5ph5Hhqt1uPtFBQUQKcKdVqmVYYiLy8PQnFxbABgGJZqnRBCCCFBIOBD1YxGI/R6vctyvV5f4VSGX331FU6ePIlPP/3Ur23iOM6v2/MnqW3B3MaagPrRP6gf/YP60T9qcz9yHAeh+P5ZFMXiII5371N0k3LEMIzLdsrrR5VKCcFWKnCk0dbKPq+s2ng8hobagz1FDkWtAcBk46HV6Tx+r4WFhdAoXQNHhQWFEEVRDmw6hjdrUz8GQm08HgOB+rHyPOlDeahaBeuVtW1CiP8FPHBUFscLB3fMZjNefPFFTJs2za9TkLIsi4iICL9tr6q4C7YR71E/+gf1o39QP/pHbexHjUYjB44EADqdzutzFcuyLudVvV5f5nbc9aNao3bKOBJ5DmFhYTXivBkotel4FEURHMvCVCpwVMjzSDQYPD4OLBYLNArnoW0apQ5m83WAsWcaAfbZBKUsp9rUj4FE/egf1I+VV14fOmbVenN+oXMRIVUn4IEjvV4Po9HosjwvL6/cwtg7duwAy7K455575NdbrVYIggCj0QiNRuPTTC/S64MVx3Fyn/E8H+jm1FjUj/5B/egf1I/+UZv70WazybOq8YIAq9WK7Oxsr7Yh8AKYUrOpGY1Gl+2U149KhcK5xhFv/3bX27bUBbX1eFSr1biUl48imw3ZJjMKrTYUWG1QKpUeHwe5ObnQKJxv8NQKLTKNeRAEQT5KGTAoKioCgFrXj9Wtth6P1Y36sfI86UOptpkoil6dX/x1LtLr9ZS9REgpAQ8cNWvWzKWWkcViwcWLFzFs2LAyX3f+/Hn8+++/6N69u8tznTt3xoIFCzB8+HCf2lQTTgQ8z9eIdgY76kf/oH70D+pH/6iN/cgwjEONo+IAkpfv0T5UTRoCZP/fZrOVuR13/ahSqZwzjmwMVCpVretvf6ptxyPDMPjw3L9ISYzH3t//xsmMTMSFhkCr1Xr8PgsLixCtSHBaplbqUJRXBIhiScYRw8jbrG39GCjUj/5B/Vh55fWhKBXFF0Wv+pl+J4RUnYAHjm699VZs2rQJ2dnZcnrhF198AYvFgt69e5f5utTUVNx3331Oy7Zs2YJ//vkHS5cuRePGjauy2YQQQki1YVlWDhgJoo91HPxQY1ilVsPkkHHE86xP2b2k5oqKjMQt4Xo0DddjYvvWKLTa8PzRH+X6R54wm8xQ6ZyLqqs5DUwmk9ftsVqt+Oabb9CkSRM0adLE69cTQoKPWOr/miQjI6PCOr1VxWAwIDY21qvXbN26Fbt378bhw4eRlpaG++67Dy+++CL69u1bRa2su4YPH46WLVti3rx5gW6KTwIeOHrooYewa9cuTJ48GZMnT0ZWVhaWLVuGQYMGOQ1VmzNnDg4cOIDffvsNgD1TqfRQtv379yM9PR1du3at1vdACCGEVCXH4tgCRJ9mVRMhQi5x5MOsagCgVmtQ6DhUrTjjiNQd4RER4LIzoVUooA1VQBBFFFqtXgWOTGYTlGFqp2VKhRoWi9nroOjZs2excuVKNG7cCJs2bfbqtYQQ4k8ZGRkYO3YszGZzQPavVquxfft2r4NHkujoaGzbtg0NGzb0c8tIbRDwwJFer8eOHTuwePFiTJs2DRqNBgMHDsTMmTOd1hMEgdIPCSGE1EmOGUe8IPgWOHIzrbm3U52r1RoIJseMo5Ip2kndEKbXIy/9qvy40Gqvv+VN4MhqsUDFlQoccWpYLBZotVp5BsCKJkoBSoamFBQUeLx/Qkhwk2dV8/IcFWi5ubkwm82477FbEZNgqNZ9X0/Lxf4tR5Cbm+tz4EilUqFt27Z+bllw43keoihCoQh4WCToBUUPNWnSBK+99lq56yxbtgzLli2rcB1CCCGktnEMHIkifAoc2TnXOPKWRq0BbyvZN28DZRzVMWFhYUi3lXyRl2+1yss9ZbFaoeCUTssUrBI23mYPGYk+DFTxMYuOEBLEalbcSBaTYEB84+hAN8Nr7oaqCYKAnTt34sCBA8jMzERcXBzuv/9+p1rC0nC3rVu3YsWKFfjjjz+QmJiI6dOno1u3bk77OHjwIN5++21cvHgRBoMB99xzD1JTU+XATV5eHtavX49vv/0Wubm5CA8PR7t27bBkyRKPnq/IpEmToNVq0bdvX7zxxhu4cuUKtm3bhtjYWGzatAk//fQTsrKyEBMTg759+2L8+PFO1zldu3bF1KlTUVRUhPfffx+CIKBnz56YNWsWtFqtvN6pU6ewatUq/PPPP6hfvz6mTZvmtj1ff/01tm3bhgsXLiA0NBS33XYbpk6dCp3OPvPoiRMnMHnyZKxZswYHDhzAsWPHEBYWhsmTJ2PAgAHYu3cvdu/ejcLCQtx2222YNWtWlV2XBUXgiBBCCCFlYxgGvFhSHNunwJFfMo7UEAVW3pxgEynjqI7R6/VysAgAjGYLAO8CRzabFRxbKnBUHEhiGcYh28CLIGkNy0wghFSAob/pYLB+/Xrs2bMHo0ePRvv27fHDDz9gzZo1KCwsxLhx4+T1bDYbFixYgAcffBDjxo3Djh078Mwzz+CDDz6AwWDPvnrrrbewYcMGPPTQQ3j88cdx4cIFbN68GYIgYMqUKQCANWvW4LvvvsOUKVMQHx+PzMxMfPfdd/J+KnreE2fPnkV6ejomTJiA0NBQxMXFITs7G3q9Hk888QTCwsJw8eJFbNu2DVlZWXj++eedXr9v3z60b98e8+fPx7///osNGzYgKipKfg9ZWVmYPn06mjVrhiVLliAvLw8rVqxwyYw9cuQIZs+ejb59+2LSpEm4cuUKNm7ciIsXL2LDhg1O665YsQKDBg3C0KFD8cEHH+CFF17AuXPncO7cOcyePRtpaWlYs2YNEhMT8eijj3rVH56iwBEhhBAS5JxqHPkaOEJJplFFw3/KolKpIPL2GjSiYL9Xp4yjuiUsLAx5xcEiAMiz2INIer3e421YbVYYTTdgthYh15QFs7UILGM/rhiGgSBKU3ELPh+rhJCaq6YNUautcnJy8M477+Dhhx/GxIkTAQDdunVDQUEB3nzzTQwfPlzOjLFarZg8eTJSUlIAAImJibj//vvx7bffYsCAASgoKMDWrVsxYsQITJ48GYA9e4fjOKxbtw4jRoyAwWDAb7/9hv79++Oee+6R29GvXz/554qe90ReXh7eeOMNxMXFycuioqIwffp0+XG7du2g1WqxcOFCzJo1CxqNxmndF154AQDQvXt3nD17Fl999ZUcONqzZw8A4OWXX5a/VCm9fQDYtm0bWrVq5ZQtpdfrMW/ePJw4cQIdO3aUl99xxx0YO3YsAKB169Y4fPgwPv/8c7z33ntQKu1fvJw4cQKHDh2qssCRr7nuhBBCCKkm9qFq9ptpXvSxxhHgMpzH24tzlUoFgbfvWygukk0ZR3VLWFgYCiwW8MXTZedZ7EEkbwJHgiDg0Nm9uJxzDu+d2IBVn08Gw9iPK8Yh40jwIHAk/S1QgImQ2kesqWPVaolff/0VNpsNd9xxh9Pyfv36oaioCH/88Ye8jGVZdOnSRX7coEEDKJVKZGRkAABOnz6NwsJC9O3bFzabTf7XqVMnmM1mnDt3DgCQlJSEgwcPYteuXfIyRxU974nmzZs7BY0A+/XQ22+/jf/85z+49dZbkZKSgnnz5oHneVy5csVp3dITcTVp0kR+n4C93zp27OiUidutWzeEhITIjwsLC/Hnn3+6zF7Xt29fcByHn3/+2Wl5586d5Z9DQ0MRERGB5ORkOWgEAA0bNkR6erqHveA9yjgihBBCgpxTxpEgej3zlESeVM3HGkdqtVoOGAm8/X/KOKpbpABRnsWKcI0aeVYrlAqFxwFEoTjg1K/VI6gf3gzDOk6F2VqEGwX2i13GIUgqCEKFx7oUMKIMBUII8a+8vDwA9mwZR9Jjo9EoL1Or1U5BDABQKBSwFH+5kJOTAwAYNWqU231JAY+ZM2dCr9fjrbfewvr16xEXF4fRo0dj2LBhHj3viYiICJdle/bskTOfOnbsCL1ej99++w0rV66U34Ok9GQQju8TsA9Va9CgQbn7zcvLgyiKLn2rUChgMBic+hZwHQ6uVCpd2qFUKl3a6k8UOCKEEEKCHMuyEFC5Gkf+mFXNnnFkv1HnKXBUJ0kXr3nW4sCR2YKw0FCPM36kwFF0aDzUSi1ilfUBALlFmQAAjuUgCLy8bkWBI8o4IoSQqiF9UXDjxg2nmdqysrKcnvdmW8uXL3fJ9gGAhIQEAPagzJNPPoknn3wSf//9N/bu3YsVK1agadOmSE5OrvB5T7g7Xxw6dAi9evWSh5sBwD///OPx+3MUFRWFGzduuCzPzs6Wfw4LCwPDMC7r2Ww25ObmetW31YWGqhFCCCFBznFWNX/UOPKVSqUCL2Uc2ShwVBfJgaPibzXzrFaElfrWszxSsFIamlbCfjyxHAu+OHDEC3yFx3rJDQAFjgipbeivOrBat24NhUKBQ4cOOS3/8ssvodVq0aJFC4+31a5dO2g0GmRkZKBly5Yu/6QC2o6aN2+OJ554AgBw4cIFr5/3htlsdsmY+vTTT33aVuvWrXHixAnk5+fLy77//nun4tg6nQ4333wzvvzyS6fX/u9//wPP82jfvr1P+65KlHFECCGEBDmWZeWaMpUJHFWWPePI/rOUeUQ1juoWOXBkthfFzrdYEaoPr/R2pQAQx3LgeRsEQYAoep5xRAipjSh0FEjh4eF48MEHsXv3biiVSrRr1w7Hjx/H/v37kZqa6jT9fEVCQ0Px2GOPYcOGDcjIyEDHjh3BMAzS0tJw5MgRLFu2DBqNBqmpqejduzeaNWsGlmXx8ccfQ6lUyoGUip73VZcuXbB3717s27cPDRo0wGeffYbLly/7tK2HHnoI7777Lp544gmMGjUKeXl52Lp1q0sW0fjx4/H0009j7ty5uOeee+RZ1Tp37uxUGDtYUOCIEEIICXJSxpEoij4HjgSeR25RllM9GW+HqqnVavC24swnyjiqk6SaCvlWe+Aoz2JFmA8p9WXdDnKcfagaL9gAwOUbYJftUI0jQkiQuZ6WW2v2OW3aNOj1enzwwQfYsWMH6tWrh+nTp2P48OFeb+uRRx5BTEwM3n77bbzzzjtQKBSoX78+UlJSoFDYwxLt2rXDJ598grS0NDAMg2bNmmHVqlVo0qSJR8/7aty4ccjOzsaWLVsAALfddhueeuopPPXUU15vKzo6Gi+//DJWr16NOXPmIDExETNnzsQrr7zitN6tt96KZcuW4bXXXsOsWbMQGhqKu+66C1OnTq3Ue6kqFDgihBBCgpwUOBIcHnvLbDHj6LmD6Ni4Lz7/7S2f2iFlHIkCFceuq1QqFdQqpRw4KuB5NChVtNMTZYV5lEoFvvzhffzfL58WPy4/cEQ1jgipfRiGsX9I1LA/a4PBALVajf1bjgRk/2q12u2Qr/KkpqYiNTUVgL3O0LFjx5yeZ1kWY8aMwZgxYzzahqPDhw+7LOvXrx/69etX5ramTZuGadOm+fx8RTZt2uR2uU6nw7x581yWl+6P0o8BYMSIERgxYoTTsuTkZLz55ptOy3r27Ony2j59+qBPnz5ltrdjx45u93ngwAGXZWX9HvyFAkeEEEJIkJMDR8VZFb4EjlQqNTo2uBP1w5uhf+tHsPnrOT5swx4kEniGAkd1WGhICAosxYEjq81pimFfSRlDDz/yMC5dugTAHjTq1KlTua+joWqE1D72QDBT4wLCsbGx2L59O3Jzqz/jCLAHrhyLWBPiTxQ4IoQQQoIcx3EQRciBo4rqvrjDsiwM2iiolVpEhtTzqR0UOCIAoNOFoMBmH0pWYLW6TAlcnpKhZUKpZ+zHduvWrZ2+laUaR4SQmiQ2NpaCNwHC83y5w5al4XDEN9R7hBBCSJCTimNXJuPIH5wCR1TjqM4KCQlBobkQAFBgsUKn03n8WjlwVGqwWslsa95lGNS0jARCSMWkv2v6+ybeGDZsGK5evVrm8+6GfBHPUeCIEEIICXKlh6r5knHkD3LgyFaScVRRDRpS++hCQlCYbwQvCDDbbF4FjqSgZ+lvhYXiCl7e3ihSxhEhtQ8rB44C3BBSo6xatQrW4vp7xP8ocEQIIYQEOX/UOPIHOXAk2ANHSqWCvhGug7Q6HQp5HkU2HgD8knEEH4/tQAVRCSFVSA4cUWCYeK558+aBbkKtRn+NhBBCSJDjOA6CKIIXAptxJGUXCbx9uJpCSd8/1UVarRYmnoepuM6RRqPx6vUMw7rUOHIJJHmIMo4IqX1KZksMcEMIITI62xJCCCFBTrqItgm+Dedx5dtNuhw4Kh6qpqRCk3WSRqOBiRdQxNszjrRarVevZxjXoWqijxlHlPFGSO1DNY4ICT4UOCKEEEKCnBw4qsRQNX9cfpceqqZSUX2jukij0cBks8FcPFRNrVZ79XqWYd0Ux6YaR4QQO+nvmv6+CQke9NdICCGEBDnp4tnKC06PvVYceCpvutryOM2qxjNQUGHsOkmtVsMiCLAIvgWOwDBlDlWjjCNCSFiY3v5/aFiAW0IIkVCOOSGEEBLkSjKOKhk4KsXbm25pqJrISxlHKr+0g9QsKpUKFhsPS3Eg09vjgGUYNxlHvgUzKXBESO2zYP5CZGZmIiIiItBNIYQUo8ARIYQQEuSkYthSxpFPxbHd3Kx7q6Q4NgORZ6BUUuCoLlIqlbDwPCzFNY68DiAyjJsaR74FRSlwREjto9FoUL9+/UA3gxDigAJHhBBCSJCTh6oJvmccMQzjEjbyNeNI4BkIAqjGUR2lVCph5Xn5eFR6OWTRn6EeChwRQoJJRkYGcnNzA7Jvg8GA2NjYgOy7qqWlpeGjjz7CkCFDEBMTU2X7effdd/Htt9/izJkzyMnJwYsvvoi+ffs6rXPw4EEsWrQIn332GcLDw6usLcGGAkeEEEJIkJMyjKRZ1XzKOAJKahwVh5C8venmOA4MUxw4sjFQ6bysbUNqBaVSCUEU5aFqCi9n12PcDVVzeM7bbRFCSDDIyMjAo2PHwmo2B2T/SrUab2zfXiuDR1evXsW2bdvQs2fPKg0cffzxxwCAHj16yD8TOwocEUIIIUHOLxlHDj9Xpp6MQqGAKNhnVlNSjaM6ScowMhXPquYYOMrOzobNZqvgwp6Rg5glKjeMkhBCAi03NxdWsxlZdw+CLSqqWvetyMpC1McfIjc3t1YGjqrLtm3bwLIs0tLSKHBUCgWOCCGEkCBXknEkOj32VmVrHAGAQqmQaxypqMZRnSQdf2beOXCUn5+Phx9+GACwceNGNGnSxO3ry8sSogwiQkhNZ4uKgjWuXqCb4bHz589j3bp1OHPmDMxmM+Li4jB48GCMHDkSL7zwAs6ePYtp06Zh/fr1uHz5Mpo0aYJZs2ahbdu2Ttv5+OOP8fbbb+PChQvQarVo1aoVZs+ejfj4eAD2jKyNGzfi+++/R2FhIerVq4ehQ4fioYceKrd9J06cwOTJkwEAjz76qLz82LFjAIBr165h7dq1+OGHH2C1WtGmTRtMmzYNLVu2lNcdMmQIUlJSUK9ePezduxdGoxFdunTBM888g+joaHk9Xycf+fjjj7FkyRLMnDkT9913n0/bCHYUOCKEEEKCXOmMI18CRwzLyplGvg5VA+xBAoFnIAqc17VtSO0gBYrMPA+OZeXjqKCgQF7HaDSWswUR2YXXcenGX2AYBvUjmiPPlA2AAkeEEFLdZs6ciYiICMydOxchISG4fPkyMjIy5OczMzOxYsUKpKamIiwsDDt37sT06dPx7rvvIjIyEgDw5ptvYsOGDRg8eDAmTpwIm82G48ePIycnB/Hx8cjNzcX48eMBAJMmTUJiYiIuXryIK1euVNi+pKQkzJo1CytXrsTzzz+PRo0ayc8VFBRg4sSJAIBZs2ZBq9Vi165dmDRpEt544w00btxYXvfrr79GvXr18PTTTyMvLw+vvPIKZs+ejddee61S/bdv3z6sWbMGzz//PO66665KbSuYUeCIEEIICXLyrGqVGqrGQB4OJFYucCQKDESB9bq2DakdpOPRwvNOQUy+OAOp9M+lWW1WfP3n+/j6z/fBsUqsfvBjnLp81Ke2+DrskhBCCJCTk4MrV65gxowZ6NWrFwCgU6dOTusYjUYsXbpUXp6cnIzBgwdjz549mDx5MvLz87Ft2zYMGTIEzz77rPy63r17yz+/9dZbyM7Oxt69e5GQkOB2P2UJDQ2VM1ibNWvmlEl08OBBXLt2DW+99RaaNm0KAOjcuTPuvfde7Ny5E/PmzZPXLSgowMsvv4ywsDAAQGxsLKZOnYrvv/8e3bp186zDStmxYwe2bduGF1980en91kZ0xUcIIYQEOTnjiK9E4IhlIBRPeV6ZjCOlUgGBB0SBoYyjOsoxA87xWBSKA5tA+YEjpUKJlKaD0bnxHfIx2LZ+Ci5knfW6LRQ4IoQQ3xkMBsTHx2Pjxo0wGo3o1KkT4uLinNYJDQ11CvKEhYWhY8eO+PXXXwEAp0+fhslkwuDBg8vcz48//oiOHTvKQSN/+fnnn9G0aVM5aAQAOp0OvXr1ws8//+y0bseOHeWgEWAPMIWEhODXX3/1KXC0efNmfPrpp3jppZfQpUsXn99DTeHbID5CCCGEVJuSGke802NvMA7lsaWbbV8CUCUZRxQ4qqscM+A4h2PIMVjkGEQqjWFYhOui0SDyJtSPaA4ACFUbfGoLBY4IIcR3DMNg7dq1aNy4MVauXInBgwdj1KhROHnypLyOuynnIyMjkZWVBcBeFByAU62g0nJzc6tkNrS8vDxEuSlEHhUV5TJkWhpW5ygiIkJ+H946dOgQmjVrhltuucWn19c0FDgihBBCgpxfZlVjHadA9/1mW6lUQigOHPlapJvUbNLxZysn46i8wJE/Vdd+CCGktmrUqBGWLl2KL7/8Eps2bYJarcZTTz2FwsJCAPbhbKXduHFDDtgYDPbAf2ZmZpn7MBgMuH79ut/brtfrcePGDZflWVlZ0Ov1TsvcrZedne028OSJVatWIS0tDc888wysVqtP26hJKHBECCGEBLnSNY58CdiwDCvXNhJE32dnUyiUEHkGgsBQjaM6ynHopOMx5GngiGGYMjOFvB0+SYEjQgjxD4VCgQ4dOmDUqFEoKCiQA0H5+fk4fvy4vF5+fj5OnDiB1q1bAwDatm0LjUaDgwcPlrntLl264MSJE7h27ZpPbZMynM1ms9PyW265BefOncP58+flZUVFRfi///s/tG/f3mndEydOID8/X378448/oqCgAG3atPGpTY0aNcL69etx5swZzJ07Fzabzaft1BR0xUcIIYQEOX/UOAJTUttIhO832/ZZ1QCRBw1Vq6PkjCNR9CnjyB4bcg4ciT4WbKfAESGE+O6vv/7CunXrcMcddyAxMREFBQXYsWMH4uPjkZiYCMCe1bN48WI89thjCA0Nxc6dOwEADz30EAB7DaTx48djw4YN4HkevXv3hiiKOHHiBPr164eWLVti+PDh+PjjjzFx4kSMHTsWCQkJSEtLw8WLFzF16tQK29mwYUNwHIcPP/wQHMdBoVCgZcuWGDhwIN5++2089dRTmDhxIrRaLd58802YzWaMGjXKaRshISF44oknMGrUKOTn52PDhg1o3bq1U32js2fPIi0tTc6ykuo4RUREoEOHDi7tat68OdatW4cpU6Zg4cKFWLhwoW/XaDUABY4IIYSQIFd6qJqvGUdScWxUosaRUqmEaLVnHNFQtbrJH0PVRJfhkr4Fjsorwk0IIYGg8LFmTiD2GRUVhcjISOzYsQPXr19HSEgI2rdvjwULFsjn+OjoaEyZMgXr16/HlStX0LRpU6xdu9ZpiNfIkSMRHh6OPXv24OOPP4ZOp0Pbtm0REREBwD5UbevWrdi4cSPWr18Ps9mM+Ph4DBs2zKN2hoeHY+bMmdi1axc++eQT8DyPY8eOISQkBJs3b8batWuxfPly2Gw2tG7dGps2bULjxo2dttG7d2/ExsZi+fLlyMvLQ+fOnfHMM884rbNv3z589NFH8uO33noLANChQwds2rTJbdtatGiBNWvW4PHHH8eSJUswd+5cnyYfCXYUOCKEEEKCXOmhaj7XOCo1q5ovlAqpxhFlHNVVlQ8cMSg9Us3XI5ICR4SQYGEwGKBUqxH18YcB2b9SrZbrDXkqMjISCxcurHC9nj17omfPnuWuM2jQIAwaNKjM5+Pi4jzaV1mGDh2KoUOHuiyvV68eli5d6tE2Ro8ejdGjR5f5/Lx58zBv3rxytzFw4EAMHDjQaVnbtm3xv//9z6M21FQUOCKEEEKCnBQ4OnQxzemxN1iGlYcDCZXIXFIolBAFQOBBNY7qqJIMOOehao5BnPICOqxDEFMiioJP39BS4IgQEixiY2Pxxvbt8ixj1c1gMCA2NjYg+ya1H13xEUIIIUGuXr16GDVqlDztbEhIiNfbYFkWgmi/yZZqHPlyo65QKCDyDHhepKFqdZT0e7cJAliuJOvM8xpHrEvWmyiKPh2P0kw2ZRXbJoSQ6hQbG0vBGy+JoljBlw1sra0bVJNQ4IgQQggJcizLYvjw4ZXaBsMy8q26VOvIlwsxhUIBUWQhCpRxVFc5Bo44pS8ZR6xLxpEg8mBZ7wOR0iw2tbCcBCGEBFxFw7b84aOPPsKiRYvKfH78+PFITU2t1D4OHDhQqdcTChwRQgghdYLjzXqlA0cCC1GkwFFd5Vis3behag6F2osJouDT8UgZR4QQUrP16tULb7zxRpnPR0dHV19jSJnoio8QQgipA1iWhVB8My9WMnAk2Bj5Z1L3yMXaecFpuKKU/QOUBHTccRw2KRFFASzj/fFosVi8fg0hhJDgYTAYvC7qTaofDRYkhBBC6gB7xpE9K0P635fAEcdx4G2s/DOpe6TjxiLwYB2OASlYxDJMuYEjjuMgCM6BI160QaHw/niSM44qMVMgIYQQQspHgSNCCCGkDnAcHiT972txbMo4qtuk37uFF5yOAbPZDAAI1WnLDRwpOAX4UhlHvOBbjSNpn6VrJhFCCCHEfyhwRAghhNQBLOcYOLLftPuecVTyM6l7pGBRkc0GziFwZLFYwDIMQrU6mEymMl/PKTjwgs1pmSDwPgUipaFq5c3iRgghhJDKoa8KCSGEkDqAY1mIxQEjKYDkS+DHHjhifH49qfnUajUYhkGB1QadTicvLyoqgkathlatKjdwxDAMcouyYLYWIddk/9/KW3wKZEoZR2Yz1ToihBBCqgoFjgghhJA6wDHjqLLFsXlryc+k7gkJCcGaNWuQm5uLJk2ayMsLCwuh06gRolajsLCwzNfn5+fhQtb/4XLSMHx+Zhd+v3YCvW8eCovV++CPFKCyUOCIEEIIqTJ0xUcIIYTUARzHwVKcccQLvg9Va9KkCUJDQ6BSq1CvXj2/tpHUHDfffLPLsoKCAoRqNAjRaFBQUFDma6NjYhCtbIz64c0wrONUmK1F+P78pzDovZ9VRwoc2Ww8eJ6nLDhCCCGkClDgiBBCCKkDHItjS0PWfLnJ7tOnD/r06ePPppFaIi8vD2E6HfQ6HS4ZjWWuFxISAmu+BmqlFrHK+gCAo39/CI1G4/U+i4qK5J9NJhNCQkK8bzghhPhJRkYGcnNzA7Jvg8GA2NjYgOy7Njt06BDmzJmD/fv3IyEhIdDNCRgKHBFCCCF1AMeVFCSuTI0jQspizM1FuE4HQ4gOv6ZdLXM9lUqJIsF5aJmVt0ClUXm9T8fAUWFhIQWOCCEBk5GRgbFjx8q116qbWq3G9u3bKXhEqgQFjgghhJA6gGVZubZRZWZVI6Qs2dnZSIiLQWRYGHJycspcT6VSwco7P28TLFCrvQ8cFRYWglMK4K1suXWVCCGkquXm5sJsNuOBGU8ipn6Dat339cuXsO/l1cjNza0zgSOTyeRTpirxDQWOCCGEkDqA4zg5YCSIAhiGBcMwAW4VqU2yb9xA1E3NEBkWhoLCQpjNZqjVapf1VCoVrKUyjiy8BXo361akoLAAah2PwlzWKfuIEEICJaZ+AyQ2axboZnjs/PnzWLduHc6cOQOz2Yy4uDgMHjwYI0eOxAsvvICzZ89i2rRpWL9+PS5fvowmTZpg1qxZaNu2rdN2Pv74Y7z99tu4cOECtFotWrVqhdmzZyM+Ph6APSNr48aN+P7771FYWIh69eph6NCheOihhyps44kTJzB58mS89NJLOHjwII4dO4bk5GSsXr0aH3/8Mfbv348LFy5AFEXcdNNNmDp1Klq3bi2/fuvWrdi9eze2bt2KFStW4I8//kBiYiKmT5+Obt26yevZbDasW7cOn3zyCXiex+23347k5GSX9uTm5mLDhg04cuQIioqK0KxZM0yYMMFpW5MmTYJWq8Wdd96Jbdu24fr16+jUqRPmz5+PwsJCLF26FKdOnUK9evUwc+ZMdOrUyevfXXUKisDRP//8g8WLF+PEiRPQarW45557MHPmzHIjiPn5+Xj99ddx5MgR/PPPP1AoFGjdujWefPJJp4OEEEIIIcWBI9gzjnjBBgUNUyN+ZLPZkJ2bixiDHjHh9iLXWVlZbutBaDQaWHnnoRxW3gS1Otzr/ebn50EdakNhrrLcgtyEEELcmzlzJiIiIjB37lyEhITg8uXLyMjIkJ/PzMzEihUrkJqairCwMOzcuRPTp0/Hu+++i8jISADAm2++iQ0bNmDw4MGYOHEibDYbjh8/jpycHMTHxyM3Nxfjx48HYA+oJCYm4uLFi7hy5YpXbV22bBkGDBiAYcOGyV9+paWl4e6770b9+vVhtVrx2WefYeLEidi9ezcaNmwov9Zms2HBggV48MEHMW7cOOzYsQPPPPMMPvjgAxgM9vPWK6+8gvfeew+pqalo0aIFPvvsM2zatMmpDTzPY8aMGbh06RImT56M2NhYvP/++3jyySexbt06pwDQn3/+CaPRiOnTpyM/Px8vvfQSXnzxRaSnp+Puu+/Gww8/jB07duDZZ5/FBx98AJ1O51V/VKeAB46MRiNGjx6NhIQErFu3Djdu3MDSpUuRk5ODVatWlfm6tLQ07N27F8OGDcPjjz8Om82GnTt34qGHHsKePXsoeEQIIYQ4KF3jiIapEX/KysqCKIqIDQ9HbHHgKCMjw23gSK1WuwaOBIvb7KSKFBTkQ6Pni3+mwBEhhHgjJycHV65cwYwZM9CrVy8AcMl8MRqNWLp0qbw8OTkZgwcPxp49ezB58mTk5+dj27ZtGDJkCJ599ln5db1795Z/fuutt5CdnY29e/fK5wVfMmxuvfVWTJkyxWmZFJACAEEQ0KVLF5w9exYHDx7E5MmT5eesVismT56MlJQUAEBiYiLuv/9+fPvttxgwYAByc3Px3nvvYdSoUXj00UcBAN26dUNqaiquX78ub+fo0aM4c+YMVq9eLW+re/fuGD58OF577TWn9yUFi6TA1F9//YW33noLs2fPxtChQwEAMTExePjhh/Hjjz869VmwCXjgaM+ePTAajThw4IAcseQ4DjNnzsSkSZPQrIw0v/r16+OLL76AVquVl/Xo0QN9+/bFrl27sHTp0mppPyGEEFITcBznVOOI4wJ+CUBqkfT0dABAXEQEYsPDAcDpG2tHarUaFptz4MhiM/kUOCosKEREvD0gmp+f7/XrCSGkLjMYDIiPj8fGjRthNBrRqVMnxMXFOa0TGhrqFAwJCwtDx44d8euvvwIATp8+DZPJhMGDB5e5nx9//BEdO3as9KxkPXr0cFn2zz//YNOmTTh16hSys7Pl5RcvXnRaj2VZdOnSRX7coEEDKJVK+Vx17tw5mM1ml5ljb7vtNpw6dUp+/PPPP0On08lBI2nbd9xxB9544w3wPC9PPnLTTTfJQSMAcgZU586dXZaVdc4MFgH/uvHIkSPo3r27HDQCgP79+0OlUuHrr78u83U6nc4paATYL0SaNWsW9J1OCCGEVDeO48CLUsYRTxlHxK9KAkfhUCuViNTr5WWl2QNHJqdlVt59PaTyiKKIwsIiKDUClGqGMo4IIcRLDMNg7dq1aNy4MVauXInBgwdj1KhROHnypLxOePGXAY4iIyORlZUFwF7vBwCio6PL3E9ubi5iYmIq3V7HmAFgzzR9/PHHce3aNTzxxBN49dVX8cYbb+Cmm26CxeJcS0+tVkOpVDotUygU8nqZmZkAgIiIiHL3mZeX57IMAKKiomCz2Zzq7YWFhTmtI+3fcbm0LFCz8Xkq4FeN586dc8kqUqlUaNiwIc6dO+fVtgoLC3H27Fk0bdrUn00khBBCajx7cWypxhEPhYIyjoj/XLt2DZF6PTQq+8xoCVGRZQaONBoNzFbnwJHZ5v3sOGazGTYbD4VKgFItUsYRIYT4oFGjRli6dCm+/PJLbNq0CWq1Gk899ZQ8U6W7WTJv3LiBqKgoAJAzaqTAizsGg8FpuJevSk/qcfr0aWRkZGDu3Lm466670L59e7Rs2dKn84EU+HLMWgLs79WRXq93WQbYh2wrFAqX5JbaIuBXjUajEXq93mW5Xq+Xo5eeWrNmDYqKijBixIhKtYkL4oKhUtuCuY01AfWjf1A/+gf1o39QP5ZPoVDINY54wQaO49z2FfWjf9S1fszIyEB8ZMm3tAmRkUi7ds3t+9fpdOAFm/04ZO2XohabCTqdzmX98vpRuqlRqAUo1ALy8/PrTH97q64dj1WF+rHyqrIP6fdSOQqFAh06dMCoUaMwc+ZMORCUn5+P48ePy8PV8vPzceLECdx///0AgLZt20Kj0eDgwYNl1hnu0qULdu/ejWvXrqFevXp+a7OUpeOYSXTq1ClcvXrV62SSZs2aQa1W4/Dhw0hKSpKX/+9//3Na75ZbbsGuXbvw3XffoXv37gDstZUOHTqEdu3a1drjMOCBo7KIoujVNMEffvghduzYgXnz5qFRo0Y+75dlWZf0tGDkLthGvEf96B/Uj/5B/egf1I/uhYaGOtU4UiqV5Z7vqB/9o6704/Xr15HgkLofHxmBkyd/dnuMSd9Sm20m6FShEEURZqsJUVFRZR6T7vpR+vZaqRbAqawwmUw14houkOrK8VjVqB8rryr6kP7+vffXX39h3bp1uOOOO5CYmIiCggLs2LED8fHxSExMBGD/XS1evBiPPfYYQkNDsXPnTgDAQw89BMB+fTF+/Hhs2LABPM+jd+/eEEURJ06cQL9+/dCyZUsMHz4cH3/8MSZOnIixY8ciISEBaWlpuHjxIqZOnepz+9u0aQOdToeVK1di1KhRuH79OrZt2+bTsDiDwYChQ4di586dUKvV8qxqV69edVovJSUFrVu3xoIFCzBp0iR5VrWLFy9i1qxZPr+XYBfwwJFer4fRaHRZnpeXV2Zh7NKOHj2KZ599FuPGjcMjjzxSqfYIguC2PcGC4zi5z3ieD3RzaizqR/+gfvQP6kf/oH4sn9VqhU2aVU3gwTCMSzo2QP3oL3WtH69cvoxbOrSXH9eLjMT1zCykp6dDVTx8TSL1h8VWBJ0qFFbeAlEUwPO8yzFZXj9evnwZAKDUCFCobcjMzHB7TJO6dzxWFerHyqvKPvTX379er69U1sj1y5f80o7q2GdUVBQiIyOxY8cOXL9+HSEhIWjfvj0WLFgg90F0dDSmTJmC9evX48qVK2jatCnWrl0rfwkAACNHjkR4eDj27NmDjz/+GDqdDm3btpWDeQaDAVu3bsXGjRuxfv16mM1mxMfHY9iwYZV631FRUXjxxRexbt06PP3002jQoAFmz56NN99806ftTZkyBTzPY9euXRAEAX369MHEiROxaNEieR2O4/Dyyy9j/fr12LhxI4qKitC8eXOsXr0aHTt2rNT7CWYBDxw1a9bMpZaRxWLBxYsXPTqQTp06halTp+Kuu+7yW4SvJpwIeJ6vEe0MdtSP/kH96B/Uj/5B/egewzAQpKFqon3Gj/L6ifrRP+pCP9psNmRmZQEQUWg2I8tohNVmP9bS09NdZtGRimCbbfYCopbi/9VqdZl95a4fpZIGSjUPpUZATm5Ore/ryqoLx2N1oH6svKrow0D/TgwGA9RqNfa9vDog+1er1U4zeHkiMjISCxcurHC9nj17omfPnuWuM2jQIAwaNKjM5+Pi4jzalzsdO3bEsWPH3D7XvXt3eciYpPTsa6mpqUhNTXV57eHDh50eK5VKPPXUU3jqqaeclg8cONDpscFgwNy5c8tt86ZNm1yWDRw40GVbAMp8b8Ek4IGjW2+9FZs2bUJ2drYckfziiy9gsVjQu3fvcl977tw5pKamokOHDli6dKlXQ9sIIYSQusRe48h+Uc0LNiqOTfxGmlln5xdfoUtSEnZ8cQg//vEngOIhbKUCR1IRbClwJP3vbUHR3NxcMAzAqUQo1QJyrwZvxjghpPaLjY3F9u3bva7T6y8GgwGxsbEB2Tep/QJ+1fjQQw9h165dmDx5MiZPnoysrCwsW7YMgwYNchqqNmfOHBw4cAC//fYbAPtFyrhx46BUKjF+/HicOXNGXlelUqFVq1bV/l4IIYSQYKVQKMCLxUPVijOOCPEHaXaZ+SMfRvPEBDwx9F7cyMvHtA2b5KCSIylAJM2sZvIxcGQ0GqHWMmAYQKnhkZ9X4HWNTEII8afY2FgK3nhJFMVys8VYlgXLBnwy+Dov4IEjvV6PHTt2YPHixZg2bRo0Gg0GDhyImTNnOq0nCILTAfX333/LhaoeffRRp3UTExPx1VdfVXnbCSGEkJqC4zhYbBYcv3AI6cZLUBgocET8Q5qqObl5M+jUauhiYtAgJgYalcrtN+9y4MhmnxXNUomMI6XGXvBdqRFgs/EoLCxESEiIr2+FEEKIg3nz5lX5Pj766COnGkKljR8/3u0wM1K9Ah44AoAmTZrgtddeK3edZcuWYdmyZfLjrl274o8//qjqphFCCCG1QlxcHKw2M9783n4uvbVF+cPBCfFUXl4eAECv0zkt14eEuJ1wpCRwZA8YmayFTss9lZubC4XGCsCecQTYs5AocEQIITVHr1698MYbb5T5fHR0dPU1hpQpKAJHhBBCCKlaPXv2xAcffCA/ViqVAWwNqU0KCgqgVipxrjgTnAGDm+snQqtWo6ioyGV9jUYDhmFgtlauxlFObg4UailwZM88ysnJQXx8vM/vhRBCSPUyGAxeF/Um1Y8CR4QQQkgdUXpadEL8wWKxAABSV68DACg5DodWLoWCZWA2m13WZxgGWo0WpuKhar5mHOXk3IBSWzxUTWvPOApUUVpCCCGkNqPAESGEEEII8ZnNZoNOo8aGaZMA2DOOAECjVMJms7l9jVajlQNGZlsRVEqV1zP95ebmwhBRUuNIWkYIIdVBFMVAN4GQSvHmGKbAESGEEEIIqRSWZZFUv77LsrIuSrVanTxEzWwthEbjXbaRKIrIM+YjujjTiGUBlZqhwBEhpMoplUowDAOLxQK1Wh3o5hDiM4vFAoZhPCpfQIEjQgghhBDiM5ZlwfOCy3JeEMrMItKF6HD4j/egUYbgq7PvICIq3Kt9FhYWgucFKNUl+1VqRQocEUKqHMdxCA8PR3Z2NgD7MHCGYQLcKkI8J4oiLBYLjEYjIiIiwHEVz7RLgSNCCCGEEOIztVoNk8WCPy5fRoOYGGQZjSg0m2GyWMusq3XLLe2QkZGBH698BF2oFh06dPBqn1KASJpNDQCUapvbWdwIIcTfpCL8OTk5NGSN1EgMwyAiIsLjCSUocEQIIYQQQnym1WphsliQunodNkydhB1fHMKPf/yJepGRZRa8HjtuLMaOG+vzPksCRyUZR5zagtzcHJ+3SQghnmIYBgkJCYiLi4PVag10cwjxmlKp9CjTSEKBI0IIIYQQ4rPQ0FAAwNrJj6F5YgKeGHovCs1mTN+4RX7O3/Ly8gA4B46UGgG5xpwq2R8hhLjDcZxXN9+E1FQUOCKEEEIIIT4zGAwAgGi9ATq1GrqYGFhtNhSaTNDr9VWyTzlwpC4ZqqZQCzBm0lA1QgghxN/YQDeAEEIIIYTUXOHh4QCArOJgDgBk5+cDACIiIqpkn3l5eeAUAOvwFahCLSA/L79K9kcIIYTUZRQ4IoQQQgghPouKigIAZDkUps7MNTo952/5+flQKIG8TCV4K4PCXAV4C4OCgkIqVEsIIYT4GQWOCCGEEEKIz3Q6HbQaDTJycuVl14uLV1dl4EgQRfx0IB75WSr8/W0ELp0yQBQBk8lUJfskhASn+g0bIiY2NtDNIKRWoxpHhBBCCCHEZwzDICYmBhnZOfKy9OwcqFRKuf6RvxUWFkITyqPtXdehM9jQvEc2blwqwrnvI1FYWFjmbG6EkNpn66uvBroJhNR6FDgihBBCCCGVEhsbi/ScHPlxenY2YmNiwTBMlezPZDJBoeYRFm2fBltnsMFSaP+5qKioSvZJCCGE1FU0VI0QQgghhFRKbFwcrmZny4+vZecgNi6uyvZXVFQIVsE7LeOUAgAaqkYIIYT4GwWOCCGEEEJIpcTFxeHaDcfAUTZiq7DmiMlsAqdwLoLNcvbHFDgihBBC/IsCR4QQQgghpFJiY2NRUFSE/OJhYunZ2Yirwowjq8UiB4ok0mOr1Vpl+yWEEELqIqpxRAghhBBCKkXKLkrPzgHLsjAWFFZpxpHVZgWjLB04sv/P87ybVxBCCCHEVxQ4IoQQQgghlVISOMoGx7JOy6qC2WyBVWCRl6kEAIRFW2EusO+XAkeEEEKIf1HgiBBCCCGEVEpERAQ4jkNGTi4UnD31JyYmpsr2l5eXB2NuCDL/CQHDirh17CVcOqUHAIiiWMGrCSGEEOINChwRQgghhJBK4TgOUZERuJ5rDxwxDIOoqKgq219YaChUkdfQ8BajvKxBOyMyL4RU2T4JIYSQuooCR4QQQgghpNKioqLx5pdfgWNZ6MPCoFBU3WUmwzKACOgMNpgLOeRlKqEKsQ9RY1ma+4UQQgjxJwocEUIIIYSQSru9b197QAfALe1uqdJ9FeQXIjs7BPlt8vHvST2yr2jR5cErAOzZT4QQQgjxHwocEUIIIYSQShs4cCAGDhxYLfuKio4CE3IVoVEWNO+RDd6aA0GwB62USmW1tIEQQgipKyiXlxBCCCGE1CihoaFglSI4pQidwYawaCtE3h44UqlUAW4dIYQQUrtQ4IgQQgghhNQoWo0OgpVxWsbb7I81Gk0gmkQIIYTUWhQ4IoQQQgghNYpWq4Vgc664wFtZ+TlCCCGE+A8FjgghhBBCSI0SEhIC3upcBJu3MPJzhBBCCPEfChwRQgghhJAaJSQkBDaL82WszcKCYQCdThegVhFCCCG1EwWOCCGEEEJIjaLX62E1Odc4spo56EK0YFm6vCWEEEL8ic6shBBCCCGkRgkLC4PNKkKwlSyzmliEhYUFrlGEEEJILUWBI0IIIYQQUqMYDAYAgMVUUufIamJhMIQHqEWEEEJI7aWoeBVCCCGEEEKCR3h4OAAgP0sFkbcCAMz5SkQ2jgpgqwghhJDaiQJHhBBCCCGkRomOjgbDMDjzRYzT8tgusQFqESGEEFJ7UeCIEEIIIYTUKBEREXjllVeQl5fntLx58+YBahEhhBBSe1HgiBBCCCGE1DhNmjQJdBMIIYSQOoGKYxNCCCGEEEIIIYQQtyhwRAghhBBCCCGEEELcosARIYQQQgghhBBCCHGLAkeEEEIIIYQQQgghxC0KHBFCCCGEEEIIIYQQtyhwRAghhBBCCCGEEELcosARIYQQQgghhBBCCHGLAkeEEEIIIYQQQgghxC0KHBFCCCGEEEIIIYQQtyhwRAghhBBCCCGEEELcYkRRFAPdiGAiiiIEQQh0M8rFcRx4ng90M2o86kf/oH70D+pH/6B+9A/qR/+gfvQP6kf/oH70D+rHygv2PmRZFgzDBLoZhAQVChwRQgghhBBCCCGEELdoqBohhBBCCCGEEEIIcYsCR4QQQgghhBBCCCHELQocEUIIIYQQQgghhBC3KHBECCGEEEIIIYQQQtyiwBEhhBBCCCGEEEIIcYsCR4QQQgghhBBCCCHELQocEUIIIYQQQgghhBC3KHBECCGEEEIIIYQQQtyiwBEhhBBCCCGEEEIIcYsCR4QQQgghhBBCCCHELQocEUIIIYQQQgghhBC3KHBECCGEEEIIIYQQQtyiwBEhhBBCCCGEEEIIcYsCRwE0cuRIDBgwABaLxeW5xx9/HL169cKhQ4eQlJQk/0tOTsZ9992Hd999F6Ioyus7rtOmTRv07NkT48aNw759+2C1WqvzbVXa119/jXHjxqFr165o06YNbrvtNixYsAAXL14EABQVFWHDhg24++67ccstt6Br164YNmwYXn75ZXkbRqMR69evx99//x2ot1Hj0PHoH9SPzqqqPxz/tWrVSl7n2LFj2Lx5c5nt+fbbbzFu3Dh07twZ7dq1w8CBA7FlyxaX9h07dgxJSUk4ffq0yzbKe646rF+/3un9d+vWDaNHj8bx48crve3Lly8jKSkJn376qbxs5MiRmDBhQqW3XRXuu+8+JCUl4dixY4Fuit8F+jgri3T89erVC4IguDyfmpqKpKQk+Zjx9X0E4v0/88wzGDhwYLXsi/oxMMpr2wsvvIDbb7+9mltUfb7++mukpqaiW7duaN26NXr06IGJEyfi8OHD8rnW19/d+++/jw8//NDfTfZov0lJSbhx40a175uQukYR6AbUZS+88AIGDx6Mbdu2YfLkyfLyb775Bp999hnWrVuH0NBQAMDSpUvRtGlTGI1GvPvuu3juuedgtVoxfPhw+XUjR47EwIEDYbPZkJGRgW+++QYLFizAvn37sH37dnlbwezll1/G5s2bceedd2LhwoWIiorClStXsH//fjz66KP46quvMHXqVPz666+YMGECWrZsCaPRiNOnT+PLL7/EjBkzANgDRxs2bMBNN92E5s2bB/hd1Qx0PPoH9aOzquoPRwzDyD//8MMP2L59OyZOnOjSlh07duDFF19Enz59sHTpUoSGhuLYsWN45ZVX8PXXX2P79u1Qq9X+7oIqodFosGPHDgDAtWvXsGnTJjz66KN47733kJSU5Nd9zZ8/HywbfN8znTt3Dr/99hsA4MMPP0TXrl0D3CL/at26Nfbu3YtmzZoFuikulEolsrOzcezYMXTv3l1efuPGDXz77bfQ6XTyMl/fRzC/f3+hfiTVZfXq1Xj11Vdx5513Yt68eYiJiUFmZia+/PJLTJo0CZs2bUKfPn183v7+/fuh0+kwaNAg/zXaA3369MHevXuh1+urdb+E1EUUOAqgJk2aYMKECdi8eTMGDhyIhg0bwmw244UXXsBtt92G/v37y9+i3nTTTWjbti0AICUlBXfffTd2797tdEMVHx+P9u3by4/vvvtuDBgwABMmTMCyZcuwePHian1/3jpy5Ag2b96MCRMm4Mknn5SXd+7cGUOGDMFXX32FCxcu4P/+7/+wfPlyDBkyRF6nf//+Tq/xN57nIQgClEplle0j0Oh49A/qR2dV3R+e+v3337F8+XLcfffdTtmJ3bp1Q/v27fHYY49h/fr1mDlzZuXecDVhWdapH9q1a4fbb78de/fuxbx585zWFUURVqsVKpXKp30Fa/D9ww8/BMdx6NKlCz777DPMmzfP5/cYjEJDQ3061quDUqlE9+7dcfDgQaeAxyeffILY2FgkJibKy3x9H8H8/v2F+rFmMZlM0Gg0gW6G1w4fPoxXX30VU6dOxbRp05yeGzBgAEaPHh2UXw54IjIyEpGRkYFuBiF1Qs38lKhFHnvsMSQkJGDhwoUAgM2bNyMzM9Plwt8Rx3Fo0aIFLl++XOH2b731VvTr1w8HDhxAfn6+39pdFbZv347o6GiXk5rk9ttvh9FoBADExMS4PC+d9C5fvoy+ffsCAKZPny4P55D6a9WqVRg0aBCSk5PRq1cvPPnkk8jIyHDaljQ0Y//+/ejfvz/atm2Ls2fPIikpCZ999pnLvocOHYrp06fLj69du4aZM2eia9euaNeuHR555BH8+uuvPvRK9aLj0T+oH51VdX9I1q9fjw0bNqCwsFD+ux85ciQAYOfOnQDgNsDcu3dvdOnSBW+99RbMZrM3by1oJCQkICIiApcvX5aHGnz99dcYPHgw2rZti0OHDgEAvvzySwwZMgRt27ZFSkoKFi5ciIKCgnK3XXqo2vr165GcnIzff/8dw4cPxy233IKBAwfim2++cXnt+++/j0GDBqFt27bo1asXXn75ZdhsNr+854MHD6Jbt24YM2YMjEYjjhw5Ij8nDbk7cOAA5s6di06dOqFbt2547bXXAAAfffQR+vfvjw4dOmDq1KnyuQUoGdpz5MgRTJs2DcnJyejduzc++OADAPZjqU+fPujcuTOee+45l2GOf/75J8aNG4fk5GR06NABkyZNwr///uu0TlJSErZu3Yp169ahR48e6Nq1K5599lkUFha6tEMaYuRuGCHgOrxGGrpx6tQpjB49Grfccgv69++Pb775BoIgYM2aNUhJSUH37t3x0ksvuR0m5YmBAwfi888/d3r/Bw8exN133+2UBehuqJQv71963ZYtW7Bq1Sp069YNnTp1wvLlyyGKIr777jvce++9SE5OxqhRo3D16lWn9npy7g8E6sfgJP0dnTx5EmPGjEH79u2xfPlyAJ5d4x04cADDhw9Hly5d0LlzZ4wcORKnTp0KxFvB66+/jpiYGEyaNMnt8+3atUObNm3cPid93peWnJyM9evXA7CfI3744QccPnxYPvdKzx0+fBhjxoxB9+7d0aFDBzzwwANOn9VA5T6zSg9Vkz4nP/jgA7zwwgvo3LkzevbsieXLl7uce86dO4dJkyahY8eO8hdIUlkMQogrChwFmEqlwgsvvICjR49i48aN2LZtG6ZPn46EhIRyX3f58mXExsZ6tI+ePXvCarXKKf3ByGaz4aeffkL37t3Lzepp2rQpdDodli1bhv/9739ub3hiY2OxYcMGAPabxL1792Lv3r1yf2VlZWHChAl49dVX8dxzz+HKlSsYOXKkywnl119/xfbt2zF9+nRs2bIF8fHxSE5OxkcffeS03oULF3DmzBl5+Exubi4efvhh/P7773j++eexfv16aLVajB49GllZWZXqp6pGx6N/UD8682d/CIIAm83m9I/neQDAAw88gPvvvx8ajUb+u58/fz4A+xC2pKQkNGjQwO2+7rjjDhQUFLhc/Lvbn6832lUpPz8fubm5cn9lZGRgyZIlGDNmDLZu3YqWLVvi0KFDmDp1Kho3bowNGzZg8uTJ+OCDDzBlyhSv92e1WjFr1iwMHToUGzZsQEREBB5//HFkZ2fL67z++uuYO3cuevbsic2bNyM1NRU7d+7EmjVrKv1+f/75Z1y6dAn33HMPUlJSEBERgf/+978u661ZswahoaFYu3YtBgwYgBUrVmDVqlXYuXMnZs2ahXnz5uH777/HypUrXV67cOFCtGzZEhs2bED79u3xzDPPYOXKlfi///s/LFy4ENOnT8cHH3yA7du3y6+5evUqHnnkEWRlZckZgRcuXMAjjzziUoNj9+7d+Pfff7Fs2TJMnjwZH374ITZu3FjpvpE888wzuOOOO7BhwwbExsbi8ccfx5IlS3D16lUsW7YMjzzyCLZs2eJyTvPU7bffDp7n5ZvAK1eu4OTJkx4PVfH1/e/evRvXr1/HypUr8eijj2L79u1YunQpXnzxRUyYMAErVqzAv//+i+eee87pdZ6e+6sb9WNwmzlzJrp3747Nmzfj3nvv9fga7/LlyxgyZAjWrl2LVatWoV69enjkkUfwzz//VGv7pevrbt26QaGomoEm8+fPR6tWrdChQwf53PvAAw8AsPfDbbfdhhUrVmD9+vXo0KEDHnvsMbd16fz5mbVmzRqwLIs1a9bgP//5D7Zv3459+/bJz1+6dAkPPfQQcnNzsWzZMqxatQo3btzAo48+6rYmIyGEhqoFhS5duuC+++7D2rVr0bp1a/kbckfSzUteXh727t0r1/jxRL169QAAmZmZfm23P+Xk5MBsNiM+Pr7c9UJDQ7FkyRLMnTsXEydOlLMS7rzzTowePRo6nQ4qlQotW7YEADRq1MglRXvp0qXyzzzPIzk5Gbfeeiu+//579OzZU34uNzcX7733ntx/AHDPPfdg5cqVyM/Pl+uyHDx4EGFhYejduzcAex0Vo9GIffv2ISoqCgDQvXt33HnnnXjttdfw9NNP+95R1YCOR/+gfnTmr/5YtWoVVq1a5bLtN998E/Xq1UO9evVchnEBQHp6erm1f6QhIenp6U7LH3zwQW/eZrWSbtSuXbuG5cuXg+d59O/fHx999BFyc3Oxbds2tGvXTl5/xowZaNu2rVPgxmAw4KmnnsKxY8e8qhFktVoxc+ZM+XOvYcOG6NevH44cOYJ7770X+fn5WLduHcaPHy9neaWkpIDjOKxYsQLjxo1DRESEz+/9ww8/hEqlQr9+/aBQKDBgwAC89957Tp/NgP1b8WeeeQaAfVji559/jt27d+Orr76S9//HH3/g3XffxaJFi5z2MWDAALkuV7t27fDFF1/go48+wueffy4Pifvhhx/w6aefyjW13njjDVitVmzfvl0ePiF9e757926njNro6Gi89NJLAOxZhKdPn8Znn33mt+GSI0eOlId5xsXFYdCgQTh9+jTeeecdAECvXr3w1Vdf4dNPP/WpLolGo8Edd9yBgwcPyv83a9YMLVq08Oj1vr7/uLg4OfNDeg87d+7ERx99JNfxSU9Px6JFi2A0GuXaJ56e+6sb9WNwGz58OMaPHy8/XrdunUfXeFOnTpVfIwgCUlJScPr0aezfv79KSyuUlpOTA4vF4nJ9LYqi/KULYM/a93W4WvPmzREaGgqdTudy7h0xYoT8syAI6Nq1K/7++2+88847Luccf35mtWvXDnPnzgVgP/d8++23+Oyzz+Ttb9iwAXq9Hq+//rpc27BDhw7o27cv9u3bh0ceecSnviCkNqPAUZB47LHH8P7772PMmDHgOM7lecebF4VCgYceesjjb4kdZyUKVlIbHdOyy3L33XcjJSUF//vf/3Ds2DF8//33WLNmDf773//ivffecyom6c7XX3+NTZs24a+//nIa5nPhwgWni56kpCSnoBFgv5FYunSpPNwDsNciuPPOO+UbiaNHj6Jr164wGAzyjR3LsujUqVPQzY5Tlrp+PPoL9aMzf/THqFGjMHjwYKdlISEhVdNgAMuXL3cpKnvmzBk5kylQCgsL0bp1a/mxwWDAvHnz0KtXL3z00UeIiIhwChoVFBTg7NmzLoHru+66C7Nnz8bx48e9ChyxLOtUk6VRo0ZQKpVy4O3kyZMoLCzEXXfd5ZSJ0K1bN5hMJvz111/o0qWL1+8bsN+sfvLJJ+jTpw/CwsIAAIMGDcJbb72Fzz//HEOHDpXX7dGjh/wzx3Fo0KABGIZxClo1btwYRqMRBQUFTseS42vDwsIQGRmJTp06OdVRaty4sdM358ePH0e3bt2cam4kJiYiOTnZZda7lJQUp8fNmzd3OxTaV47tb9y4MQA4/c4Aew2yymRADBo0CFOnTkVBwf+3d+fRNZ3748ffmU5kPBIZ0HwNQY8QQhIkQQjCJUsrbkurjaFmCYpektbQxK2aaYYi15RplXKzaKpFxdQaqi5uu1arSGsWGQSJiAzy+8Pv7JuTnGhCJMHntVb+OHvej+3Ze3/283yee3z99dfVCkA96fnrO4esrCyd/6fa801PT1cCHlW999cFKcf6Sxsc16rqM15aWhorV67k9OnTOi2RLl68WCvHrVXZ8/WePXt0Uiy88847j+0+/qTS09NZtWoVR48eJTMzUzmesvcvrZqss8pfi61bt+ann35Sfh85coRBgwZhZGSk/DtaW1uj0Wiei9QSQtQFCRzVE9ruWZV109K+vFhaWvLKK69UKwGo9kFeX16g+sLGxgZTU1OuX79epeXVajVDhgxhyJAhlJaWEhkZyeeff8727dsZOXJkpev9/PPPTJkyhb59+zJ+/HgaNWqEgYEBw4YNq5DbRPslqSw7Ozu8vLzYtWsXQ4YM4ezZs1y4cIEPP/xQWSYnJ4czZ87ovSk2a9asSudX117267GmSDnqqonyaNy4sZJAuzocHR0fW79cu3ZNWa6sVq1aVdhf2fwhdaVBgwYkJiYqQZAmTZrofC0uX3/l5uZSWlqKnZ2dznRjY2MaNmzInTt3qr3/8v8+JiYmSj2q7bIWGBiod/3yeVOq48iRI2RnZ+Pn56fkJmrdujWNGzcmJSVFJ3CkDSyVPcbyHxe01+ODBw90Akfl11WpVBVG7jExMdHp1nD37l2lxWtZdnZ2FV52/mpbT6vs8Wv/rWp6nz4+PlhYWPD5559z7ty5anW1e9Jj0beevmmAcj1W595fF6Qca4+RkZFOS5uySkpKKnTnKl+XVuUZLy8vj/feew9bW1tCQ0Np2rQppqamzJ07t9bLycbGBpVKRXp6us50b29vtm/fDlBp7qOn9fDhQyZPnkxubi7Tpk2jefPmmJmZERkZqfceUJN1lr66v+x6OTk5xMXFKaOTlvU8JkAXojZI4Og5oe/lpaq+//57VCqV3ptcfWFsbIyHhwfHjh2jqKioWqOXGRgYMHbsWD7//HPS0tIeu+y+ffuwtLRU+j7D/14Y9W1Xn4CAABYsWEBOTg67du1SgklaarWanj176nzJ0XpRRvx50a/H2iLlqOtpyuOvdO3alZ07d3LlyhW9eY7279+PhYVFpQlC6xtDQ8PHllX5+svKygoDA4MKedaKi4u5ffs2arW6Ro9Pu73o6OgKLTcBnJycnnjbKSkpAISFhREWFqYzLyMjg8zMzCfe9tNSq9V6u49mZWU9dRlru1MUFRXpTK9u0K8mGRkZMXDgQDZu3Ejnzp0rzSFW16pz768LUo61x9bWttIu3pmZmX85QldVnvHOnDlDeno669at0+lymJubq7c+fJaMjY1xd3fn2LFjlJSUKK191Wq1cg953LOpqalphTqnsLCQ+/fv/+W+L126xK+//kpMTAz9+vVTphcUFDzJqdQotVpNr169GDFiRIV5z7IVsxDPM0mO/YI7fPgw3333HYGBgX/ZhauujRkzhqysLGJiYvTOP3DgAHl5eXpvONqmv9rWF+W/kmkVFBRgYmKi81KlfQmpqv79+2NgYMCePXv45ptvGDhwoE63Gx8fH9LS0pSX4LJ/j8ux8jJ4nq7H+kzKsXKVfY0MCgqitLRUb2LmH374gePHjzNixAjl5fxFY2FhgYuLC99++63O9L1791JcXIynp2eN7s/d3R0zMzPS09Mr1IMdOnR44vxG9+/fZ9++ffTr14/4+Hidv9WrV/Pw4cMnTvZcEzw8PDh+/LhOkvAbN25w+vTppy7jRo0aYWJiovOBpLCwsEIXuNr2xhtv4Ofnx+jRo+v0OB6nJu79z5qUY+3o0qULd+/e1em2BI+COidOnKBLly6PXb8qz3ja59SyH0FPnTpVZ0G2MWPGkJGRwdq1a6u9rqOjI0VFRTqjjR09erRCd/myLU61tL/LloM2+Xtd8/b25vz587Rr167Cv6Ozs3NdH54Q9ZK0OHqB3LhxgzNnzlBSUkJmZiaHDx9m586duLm5MWfOnLo+vL/k6+vLpEmTWLNmDX/88QcBAQE0atSIa9eu8dVXX/Hnn3/y2WefMXnyZAIDA/Hw8MDc3Jy0tDRiY2OxsrJSukXY29tjbW3Nrl27cHJyQqVSodFo6N69O3FxcSxcuBB/f39Onz6tDLFcVdpE2DExMWRkZBAQEKAzf/To0aSkpPDuu+8ycuRImjZtyq1bt/jvf/+Lo6NjvX4orEnP+/VYX0g56tKWR3nt2rVDpVLRqlUriouLiYuLo3PnzlhaWuLs7IyLiwtz5sxh0aJF3Lt3jzfffBNLS0tOnDjBhg0b8PT01Elc/CIKCQkhODiYmTNnMmTIEK5cucLKlSvx9vauVn6jqrCysmLatGksW7aM9PR0unXrhqGhIVeuXCE1NVUZiai69u/fT35+PkFBQXqPecOGDaSkpOh83a5No0ePJjk5mbFjxzJp0iRKSkqIiopCrVY/dbJVQ0ND/P39SUpKonnz5tjY2JCQkFCl3IDPkouLS42OBvcs1MS9/1mTcqwdPXr0wNPTU6kP27RpQ0ZGBuvXr8fY2FjvwA1lVeUZr1OnTpibmxMeHs6ECRO4efMm0dHRFbpC15bevXszYcIEIiMjOXv2LAMHDsTBwYHc3FxOnjxJZmZmpa1sfH19MTc3Z+7cuYwfP5709HTi4+Mr9AxwdnZmx44d7N+/H3t7exwcHHB2dqZx48asWLGChw8fcv/+fSIjI6s8euyzNG3aNN544w3Gjh3LsGHDsLOzIysrixMnTuDp6amMlCyE+B8JHL1AEhISSEhIwMTEhIYNG6LRaAgPD2fIkCHPbAjOmjZjxgw6d+5MQkIC8+bN4969ezg4OODj40NYWBgODg4MHz6cI0eOsG3bNu7du4ejoyNeXl5MmjRJGRnJ0NCQRYsWsWrVKmVozdTUVHr16sUHH3xAYmIiycnJuLu7s27dOgYMGFCt4wwICOC7775Tkp6WZWNjw9atW1m9ejXLly/n9u3bNGrUCDc3N/z9/WusrOq7F+F6rA+kHHVpy6O81NRUnJyc8PPzY8SIEcTGxpKdnU2XLl2U5UeNGkXr1q3ZuHEjoaGhFBQU0KxZMyZPnsyYMWNemK6klenbty9RUVHExMQwZcoUrK2tee2112psFK/y3nvvPRwdHdm0aROJiYkYGxvTrFkzevfuXa3uyGWlpKTQtGnTSgNdgYGBREREVOhaUVuaNGlCYmIiS5cuZfbs2RgYGNCtWzdCQ0P/sgtMVcybN4958+bxz3/+EwsLC8aNG0fz5s05ePDg0x/8C6ym7v0vuxehHA0NDVm3bh2RkZFs2rSJjIwMLC0t8fLyIioq6i+DGlV5xrOzs+Ozzz5j6dKlTJkyhRYtWvDxxx+zfv362jhFvWbNmoWHhwdJSUmEh4eTl5eHWq2mffv2LFq0qMJHUC0bGxsiIyNZsmQJwcHBuLi4sGzZMmV0Mq3x48dz+fJl5syZw927dwkJCWHq1KlERUURERHB9OnTadKkCZMnT+b48eN1noC6efPmbNu2jdWrVxMeHk5+fj729vZ06dLlpe8dIERlDEqfx6F5hBBCCCGEEEIIIcQzJzmOhBBCCCGEEEIIIYReEjgSQgghhBBCCCGEEHpJ4EgIIYQQQgghhBBC6CWBIyGEEEIIIYQQQgihlwSOhHhJRUVFodFolL9u3brx9ttvc+jQobo+tHqjbBm1bdsWDw8PBg8eTEREBGlpaTrLBgUFMXHixDo6UkhOTkaj0XDr1q06OwYhnidfffUVb7zxBh4eHri7uzNw4EA++ugjsrOzq7Wdq1evotFo2L17tzKtsLCQsLAwvLy80Gg0bN68uUrbunv3LlFRUVy4cKFax1Bflb/PlP3TDj0fFRXFqVOn9K6fm5vLihUrGDBgAK6urnTr1o2QkBC9IzI9rg6u6/r5cQ4dOsT48ePx8vKiffv2+Pj4MGnSJA4ePIh2/JrQ0FAZHlwIIUSdevnGchZCKBo0aEBcXBwAGRkZxMbGMmnSJJKSknB3d6/jo6sfypbRvXv3OHfuHFu3buXLL7/kk08+4fXXXwdgwYIFGBrWXSy+d+/ebN26FWtr6zo7BiGeF7GxsaxcuZLRo0czbdo0SktLOX/+PCkpKWRkZNCoUaOn2n5ycjI7d+5k8eLFNGvWjFdeeaVK6929e5fo6GjatGlD69atn+oY6ouydWhZTZo0ASA6Ohpzc/MK95zs7GzeffddsrOzmThxIh06dCArK4uEhATeeustVq9eTb9+/WrlHJ6VlStXsm7dOvz9/Zk/fz729vZkZWWxb98+Jk+ezJo1a+jdu3ddH6YQQgghgSMhXmaGhoZ06tRJ+d2pUyd8fX3ZsWOHBI7+v/Jl1L17d0aMGMGECRP46KOPcHd35//+7//q/CXP1tYWW1vbOj0GIZ4XCQkJBAYGEhoaqkzr1asX48aN4+HDh0+9/bS0NBwcHHjttdeeelvPu/J1aFWFh4dz5coV/v3vf6PRaJTp/v7+jBo1ijlz5rBnzx7s7Oxq8Ghrz8GDB1m3bh0hISFMnTpVZ97AgQMZNWpUnX6MEEIIIcqSO5IQQuHg4ICtrS3Xr18HHrVCCgsLo2/fvnTs2JH+/fuzcuVKCgsLddbTaDTExsaydOlSvLy86Ny5M6GhoeTl5SnL5OfnExERwYABA3Bzc6NPnz7Mnz+f3NzcWj3HmmBqasq8efMoKipi27ZtQMWuEGlpacyYMYNevXrh5ubGoEGD2Lhxo85L6Y8//ohGo+GXX37R2f7EiRMJCgoCoLi4mL///e8MGzaMkpISZZn169fj6urKb7/9BujvqlZYWMiqVavo27cvrq6u+Pr6EhYWVvMFIsRzJjc3FwcHB73zyr6sazQaNmzYoDN/w4YNOoGM8vr06UN8fDw3btxQumVdvXr1L+uEq1ev0rdvXwCmT5+usy48+v+8cuVK/Pz8cHV1ZeDAgaSkpDxVOdQ1bTkuXbpUOd8ff/yR69evs3fvXgYPHlyhrE1MTHj//ffJy8tT6t/n0aZNm7C3t2fy5Ml653fs2BFXV1e986KioujcuXOF6Z07dyYqKkpn2sGDB3nrrbdwc3OjS5cuBAUF8euvvyrzr1+/zrRp0/D09MTNzY2RI0dWuCelpqYydOhQOnfujKenJ0OHDq3QrT05OZnBgwfToUMHevbsyapVqyguLq5SWQghhKj/pMWREEJx79497ty5Q7NmzQDIycmhYcOGhIWFYW1tzcWLF4mKiiIzM5NPP/1UZ92EhATat2/PkiVLuHr1KsuXL+fBgwesWrUKgIKCAkpKSpgxYwa2trbcuHGDtWvXEhwcTHx8fK2f69Nq3bo1jo6OnD59Wu/8jIwMWrZsyeDBg7GwsOC3334jKiqK/Px8QkJCqrwfY2Njli5dytChQ5Xy+v333/nss8+YOnUqLi4ula47depUjh8/zsSJE+nUqRO3bt1i79691T5XIV407du3Z8uWLTg5OdG7d2/s7e1rbNvR0dGsXbuWU6dOER0dDTwKyv/nP/95bJ3g4OBAdHQ0ISEhzJw5k27duinrwqNg0qlTpwgODqZVq1YcOnSIf/zjH1hbW9OrV68aO/5nQV8AwcjIiK1btzJ8+HCCgoKUHD6tW7dm3759lJaWVtoVrWvXrlhbW3PixAmdwEtpaanefWlzBdUXxcXFnDp1igEDBmBs/Owexb/55htmzpxJ3759WbFiBSYmJpw6dYqbN2/Srl078vLyePfddwGYP38+5ubmrF+/npEjR7J9+3ZatWrF5cuXmT59OgEBAcyaNYuHDx9y9uxZ7ty5o+xn06ZNLFu2jFGjRhEaGkpaWhqrVq2ipKSEDz744JmdnxBCiNojgSMhXnLah+zMzEyWL1+OpaUlI0eOBB59DZ4zZ46yrLu7O2ZmZoSGhjJ//nzMzMyUeSqVipiYGIyMjJTf8+bNIyQkhFatWmFra0t4eLjOfp2cnBgxYgR//vknLVu2rI3TrVFNmjQhKytL7zxvb2+8vb2BRy8tHh4eFBQUkJiYWK3AEUCrVq2YNWsWS5Yswdvbm/DwcNq3b8+4ceMqXefIkSMcPHiQFStW6CRVlQSrQjzKSRYSEsLcuXMBcHJyws/Pj9GjR+Pk5PRU227Xrh12dnaoVCqdLlp/VSeoVColENy8eXOddY8fP87+/fvZsGEDPXr0AB51m7158yZRUVH1OnCUn59P+/btK0yPj49XgmNNmjTROd+MjAyAx+aGatq0Kenp6TrTDh06pHdfQL3KFXT79m0KCwuVPE9apaWlOi1LDQ0Nn7i7WmlpKUuWLKF79+7ExMQo08teK8nJyVy/fp2UlBTatGkDPLpO+/Tpw7/+9S8WL17Mr7/+SlFREfPmzcPS0hKAnj17KtvIy8sjMjKScePGMXPmTODRtWlkZMTSpUsZO3YsNjY2T3QOQggh6g8JHAnxEiv/QG9kZMSaNWto0aIF8OjBMy4uji+//JKrV6/y4MEDZdkrV67w6quvKr/9/PyUoBFA//79mTt3Lr/88gutWrUCYMeOHWzevJlLly6Rn5+vLHvx4sXnMnBUWlqKgYGB3nkPHjxg3bp1pKSkcOPGDYqKipR59+7dw8LColr7CgoK4sCBA4waNQpjY2N27typU97lHTt2DDMzMwICAqq1HyFeBq+++ipff/01x44d44cffuCnn34iISGB5ORkkpKSHtuS70k9TZ1w5MgRGjZsiJeXl06LGm9vbxYuXEhJSclj64O61KBBAxITEytMr4k6v3z96+Hhobc77oIFC556XzVJ2wKq/PHv2bOH6dOnK7/feecd5s+f/0T7+OOPP0hPT9f5+FPeyZMnadOmjRI0ArCwsMDPz4+TJ08Cjz4gGRkZ8cEHHzBs2DC6dOmClZWVsvzp06fJz8/nb3/7m8616eXlRUFBAefPn6dr165PdA5CCCHqDwkcCfES0z7Ql5aWcvHiRVasWMHs2bNJSUnBwcGBuLg4lixZwrhx4+jWrRvW1tb88ssvRERE6ASRgAqjEKnVakxMTJQvx9999x1z5sxh+PDhzJgxg4YNG5KZmUlwcHCFbT0v0tPTlSBbecuWLWPbtm0EBwfj6uqKlZUVqamprFmzhgcPHlQ7cGRgYMCgQYM4evQoffr0UboTVub27dvY29tXGtgS4mWnUqno1auX0gLj+++/Z+LEicTExChdzGrS09QJOTk53L59u9LWNJmZmTRu3LjGj7kmGBoa0qFDh2qto+2ed+3aNdq2bat3mevXr1fIAWRlZaV3X9Wtb581GxsbVCpVhRZT3t7ebN++HaDS3EdVdfv2bYBKc3nBo1H89CUXt7OzU7qitWzZkrVr1yqJvA0NDenRowfz58+nadOm5OTkABAYGKh3Hzdu3Hiq8xBCCFE/SOBIiJdY2Qf6jh074uzszJtvvklMTAzh4eHs3r2bPn36MGvWLGWdtLQ0vdvKzs7W+X3nzh2KioqUh9bdu3fj4uJCRESEssyJEydq+pRqzfnz57l582alD8u7d+9m+PDhTJgwQZlWPpmoqakpgE7LA3hUdiYmJjrTtF0J27Vrx549ezh27JjS7UUfbWDuca2ihBD/07NnT9q2batTx6lUKr3/P59EVeqEyqjVamxtbYmNjdU7/0UbUbFr164YGBiwf/9+JWF4WSdPnuTu3bvPbUsWY2Nj3N3dOXbsmE5rMbVardyTVSpVpeubmppWuC4LCwu5f/++8rthw4bA/7r96aNWq/njjz8qTM/KykKtViu/fX198fX1JS8vj8OHD/Ppp58SFhZGXFycslx0dLTe4OXTdv0UQghRP8ioakIIhaurKwEBASQnJ5OZmUlBQUGFAEZlo/gcOHBAJzfD3r17MTAwUB6Cq7Ot+u7BgwcsXLgQlUrFm2++WekyZc+3pKSEXbt26Syjfcgu+6KanZ3N77//XmF7H330EWq1mqSkJPz9/QkLC3vsiHQ+Pj7cv3+fb7/9tlrnJsTLQF9usoKCAm7cuKHTAqNx48YVguVHjx59on1WpU7Qzi/fCtPHx4dbt25hYmJChw4dKvw9LshQ35mYmFQ436ZNm9K/f3927tzJuXPndOYVFxezevVqLC0tK61/nwdjxowhIyODtWvXVntdR0dHioqKuHz5sjLt6NGjOknAnZ2dady4McnJyZVux8PDg/Pnz3PhwgVlWn5+PgcOHMDT07PC8paWlgwaNIiAgADl/4U292F6errea1PyGwkhxItBWhwJIXRMmTKFXbt2ERcXh4+PD/Hx8SQmJtKiRQtSUlK4dOmS3vUKCwsJDg7m7bffVkZVGzBggJLfyMfHh4iICKKjo3F3d+fw4cMcO3asNk/tiTx8+JAzZ84Ajx6oz507x9atW7ly5QqLFy+u9Guqj48P27Zto3Xr1tja2pKUlERhYaHOMo0bN8bNzY2YmBisrKwwMjIiNjZWSUCqtWXLFn744QcSExMxNzcnPDycwYMHExERwbJlyyrdf69evfjwww+5fPkybm5u3L59m7179yoj3Qnxsho8eDB+fn706NEDBwcHMjIySEhIICcnh1GjRinLDRgwgLi4ODp27EiLFi3YsWNHpQnx/0pV6gR7e3usra3ZtWsXTk5OqFQqNBoN3bt3x8/Pj3HjxjFu3Dg0Gg3379/nwoULXLp0iU8++eSpyuNZKluHlmVra0uzZs1wdnYmNTUVT09PzMzMaNmyJZaWlixYsIBz584RFBTExIkTcXV1JTs7m4SEBH7++WdWr16tt5vV86J3795MmDCByMhIzp49y8CBA3FwcCA3N5eTJ0+SmZlZaRc7X19fzM3NmTt3LuPHjyc9PZ34+HidwKSBgQFz5sxh5syZTJ06lddffx2VSsWZM2fo0KEDfn5+DB06lM2bNzNx4kTef/99ZVS1Bw8eMH78eODR/ef06dP4+vpib2/P1atX+eqrr+jevTvwqHvgtGnTWLZsGenp6XTr1g1DQ0OuXLlCamoqUVFROgNpCCGEeD5J4EgIocPZ2ZmAgAC++OILDh48SE5ODpGRkcCjl6i5c+cyadKkCusFBQVx69YtZs+eTWFhIf7+/jpJPd966y2uXr1KUlISGzdupEePHqxYsYJhw4bV2rk9iYKCAoYPH46BgQHm5ua88soreHt7Ex0drQTF9Jk3bx4LFixg4cKFmJmZERgYiL+/vzKKk9by5cuZO3cuYWFh2Nvb8/7777Nz504lefjly5dZsmQJY8eOxd3dHXj0wrVw4UImT55Mv379GDBggN5jiIqKIjo6mq1btxIdHU2jRo2Uh30hXmYhISEcOHCAxYsXc+vWLWxsbNBoNGzevBkvLy9luSlTppCdnU10dDSGhoYMGzaMtm3bsnz58mrvsyp1gqGhIYsWLWLVqlWMHj2awsJCUlNTcXJyIjIyktjYWL744guuXbuGlZUVbdq0YejQoTVSJs+Ktg4tLzAwkMWLFzN//nwWLVrE+PHjKSgoUEZba9SoEdu2bWPdunVs2bKF69evY2FhgaenJ1u2bKmQ3+h5NGvWLDw8PEhKSiI8PJy8vDzUajXt27dn0aJFlQ5uYGNjQ2RkJEuWLCE4OBgXFxeWLVvG22+/rbPcoEGDaNCgAWvXrmXmzJmYmprSrl07/P39gUctiBITE1m8eDEff/wxxcXFdOzYkfj4eOX+ptFoOHDgAJ9++qmSOy8gIEAnifd7772Ho6MjmzZtIjExEWNjY5o1a0bv3r0rtDQWQgjxfDIoLduuVQghnoBGo2H27NmMHTu2rg9FCCGEEEIIIUQNkhxHQgghhBBCCCGEEEIvCRwJIYQQQgghhBBCCL2kq5oQQgghhBBCCCGE0EtaHAkhhBBCCCGEEEIIvSRwJIQQQgghhBBCCCH0ksCREEIIIYQQQgghhNBLAkdCCCGEEEIIIYQQQi8JHAkhhBBCCCGEEEIIvSRwJIQQQgghhBBCCCH0ksCREEIIIYQQQgghhNBLAkdCCCGEEEIIIYQQQi8JHAkhhBBCCCGEEEIIvf4fXMbSdGNVBYkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the performance\n",
    "\n",
    "method_order = ['bonobo_top1k', 'bonobo_sparse', 'bonobo_random','sweet_top1k', 'sweet_sparse', 'sweet_random', 'lioness_top1k','lioness_random', 'spcc_top1k', 'spcc_random']\n",
    "perf_correlation_random = perf_correlation[perf_correlation.method.isin(method_order)]\n",
    "\n",
    "sweet_color = '#377eb8'\n",
    "bonobo_color = \"#e41a1c\"\n",
    "lioness_color = '#4daf4a'\n",
    "\n",
    "\n",
    "bonobo_color = '#f8776d'\n",
    "bonobo_sparse_color = '#ffbab5'\n",
    "bonobo_random_color = '#f51707'\n",
    "lioness_color = '#7cae00'\n",
    "spcc_color = '#00bfc4'\n",
    "sweet_color = '#c77cff'\n",
    "sweet_sparse_color = '#d3a9ff'\n",
    "sweet_random_color = '#dfcfff'\n",
    "lioness_random_color = '#b2df8a'\n",
    "spcc_random_color = '#b2ebed'\n",
    "\n",
    "color_order = [bonobo_color, bonobo_sparse_color, bonobo_random_color, sweet_color, sweet_sparse_color, sweet_random_color, lioness_color, lioness_random_color, spcc_color, spcc_random_color]\n",
    "\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "sns.set_context(\"notebook\")\n",
    "sns.set(rc={'axes.facecolor':'#EBEBEB'})\n",
    "\n",
    "\n",
    "f, ax = plt.subplots(2, 1, figsize = (10, 8))\n",
    "\n",
    "sns.violinplot(data = perf_correlation_random[perf_correlation_random.check == 'genetics'].sort_values(by = 'auc', ascending = False), \n",
    "                            y = 'auc', x = 'group', hue = 'method', ax = ax[0],\n",
    "                            #xmin = 0, xmax = 1, \n",
    "                            cut = 0, linewidth = 1, scale = 'width', inner = 'quartile',\n",
    "                            width = .7,\n",
    "                            hue_order = method_order,\n",
    "                            palette = color_order\n",
    "                            )\n",
    "\n",
    "sns.violinplot(data = perf_correlation_random[perf_correlation_random.check == 'media'].sort_values(by = 'auc', ascending = False), \n",
    "                            y = 'auc', x = 'group', hue = 'method', ax = ax[1],\n",
    "                            width = .7,\n",
    "                            #xmin = 0, xmax = 1, \n",
    "                            cut = 0, linewidth = 1, scale = 'width', inner = 'quartile',\n",
    "                            hue_order = method_order,\n",
    "                            palette = color_order\n",
    "                            )\n",
    "\n",
    "\n",
    "# rotate xtick labels\n",
    "#g1.set_xticklabels(rotation=90)\n",
    "\n",
    "#for ax in g1.axes.flatten():\n",
    "#    for c in ax.collections:\n",
    "        #c.set_edgecolor(c.get_facecolor())\n",
    "\n",
    "xticks = g1.axes.flat[0].get_xticks()\n",
    "# add horizontal grid lines\n",
    "for axis in ax:\n",
    "    #axis.set_ylim(0, 1.1)\n",
    "    axis.xaxis.grid(True)\n",
    "    axis.axhline(0.5, ls = '--',lw= 2, color = 'red', alpha = 0.5)\n",
    "    axis.set_xlabel('')\n",
    "    #axis.tick_params(labelbottom=True)\n",
    "    #axis.annotate('Genotype', xy=(4, 1), xytext=(4, .9), fontsize=14, color = '#636363')\n",
    "    #axis.annotate('Media', xy=(16, .1), xytext=(16, .1), fontsize=14, color = '#636363')\n",
    "    #axis.set_ylim(-1, 1)\n",
    "\n",
    "ax[0].set_title('Genetics')\n",
    "ax[1].set_title('Media')\n",
    "\n",
    "ax[1].set_xticklabels([media_label_dictionary[i.get_text()] for i in ax[1].get_xticklabels()])\n",
    "    \n",
    "plt.tight_layout()\n",
    "\n",
    "sns.move_legend(ax[0], \"upper left\", bbox_to_anchor=(1, 1), title='Network', )\n",
    "sns.move_legend(ax[1], \"upper left\", bbox_to_anchor=(1, 1), title='Network', )\n",
    "\n",
    "#g1.ax.yaxis.grid(True)\n",
    "# Add y axis line for 0.5\n",
    "#g1.ax.axhline(0.5, ls = '--',lw= 2, color = 'red', alpha = 0.5)\n",
    "f.savefig(results_figures + 'network_correlation_performance_ggplotcolors_withrandom.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use this to compare random edges only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Plot the performance\n",
    "\n",
    "# method_order = ['bonobo_random','sweet_random','lioness_random', 'spcc_random']\n",
    "# perf_correlation_random = perf_correlation[perf_correlation.method.isin(method_order)]\n",
    "\n",
    "# sweet_color = '#377eb8'\n",
    "# bonobo_color = \"#e41a1c\"\n",
    "# lioness_color = '#4daf4a'\n",
    "\n",
    "\n",
    "\n",
    "# bonobo_color = '#f8776d'\n",
    "# bonobo_sparse_color = '#ffbab5'\n",
    "# bonobo_random_color = '#f51707'\n",
    "# lioness_color = '#7cae00'\n",
    "# spcc_color = '#00bfc4'\n",
    "# sweet_color = '#c77cff'\n",
    "# sweet_sparse_color = '#d3a9ff'\n",
    "# sweet_random_color = '#f2e0ff'\n",
    "# lioness_random_color = '#b2df8a'\n",
    "# spcc_random_color = '#b2ebed'\n",
    "\n",
    "# color_order = [bonobo_random_color,sweet_random_color, lioness_random_color, spcc_random_color]\n",
    "\n",
    "# sns.set_theme(style=\"darkgrid\")\n",
    "# sns.set_context(\"notebook\")\n",
    "# sns.set(rc={'axes.facecolor':'#EBEBEB'})\n",
    "\n",
    "\n",
    "# f, ax = plt.subplots(2, 1, figsize = (10, 8))\n",
    "\n",
    "# sns.violinplot(data = perf_correlation_random[perf_correlation_random.check == 'genetics'].sort_values(by = 'auc', ascending = False), \n",
    "#                             y = 'auc', x = 'group', hue = 'method', ax = ax[0],\n",
    "#                             #xmin = 0, xmax = 1, \n",
    "#                             cut = 0, linewidth = 1, scale = 'width', inner = 'quartile',\n",
    "#                             width = .7,\n",
    "#                             hue_order = method_order,\n",
    "#                             palette = color_order\n",
    "#                             )\n",
    "\n",
    "# sns.violinplot(data = perf_correlation_random[perf_correlation_random.check == 'media'].sort_values(by = 'auc', ascending = False), \n",
    "#                             y = 'auc', x = 'group', hue = 'method', ax = ax[1],\n",
    "#                             width = .7,\n",
    "#                             #xmin = 0, xmax = 1, \n",
    "#                             cut = 0, linewidth = 1, scale = 'width', inner = 'quartile',\n",
    "#                             hue_order = method_order,\n",
    "#                             palette = color_order\n",
    "#                             )\n",
    "\n",
    "\n",
    "# # rotate xtick labels\n",
    "# #g1.set_xticklabels(rotation=90)\n",
    "\n",
    "# #for ax in g1.axes.flatten():\n",
    "# #    for c in ax.collections:\n",
    "#         #c.set_edgecolor(c.get_facecolor())\n",
    "\n",
    "# xticks = g1.axes.flat[0].get_xticks()\n",
    "# # add horizontal grid lines\n",
    "# for axis in ax:\n",
    "#     #axis.set_ylim(0, 1.1)\n",
    "#     axis.xaxis.grid(True)\n",
    "#     axis.axhline(0.5, ls = '--',lw= 2, color = 'red', alpha = 0.5)\n",
    "#     axis.set_xlabel('')\n",
    "#     #axis.tick_params(labelbottom=True)\n",
    "#     #axis.annotate('Genotype', xy=(4, 1), xytext=(4, .9), fontsize=14, color = '#636363')\n",
    "#     #axis.annotate('Media', xy=(16, .1), xytext=(16, .1), fontsize=14, color = '#636363')\n",
    "#     #axis.set_ylim(-1, 1)\n",
    "\n",
    "# ax[0].set_title('Genetics')\n",
    "# ax[1].set_title('Media')\n",
    "\n",
    "# ax[1].set_xticklabels([media_label_dictionary[i.get_text()] for i in ax[1].get_xticklabels()])\n",
    "    \n",
    "# plt.tight_layout()\n",
    "\n",
    "# sns.move_legend(g1, \"upper left\", bbox_to_anchor=(1, 1), title='Network', )\n",
    "\n",
    "# #g1.ax.yaxis.grid(True)\n",
    "# # Add y axis line for 0.5\n",
    "# #g1.ax.axhline(0.5, ls = '--',lw= 2, color = 'red', alpha = 0.5)\n",
    "# f.savefig(results_figures + 'network_correlation_performance_ggplotcolors_randomonly.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>metric</th>\n",
       "      <th>value</th>\n",
       "      <th>check</th>\n",
       "      <th>clustering_method</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adjusted_rand_score</td>\n",
       "      <td>0.538265</td>\n",
       "      <td>media</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adjusted_rand_score</td>\n",
       "      <td>-0.045111</td>\n",
       "      <td>genetics</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adjusted_mutual_info_score</td>\n",
       "      <td>0.716702</td>\n",
       "      <td>media</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adjusted_mutual_info_score</td>\n",
       "      <td>-0.141768</td>\n",
       "      <td>genetics</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>completeness_score</td>\n",
       "      <td>0.799636</td>\n",
       "      <td>media</td>\n",
       "      <td>kmeans</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>adjusted_mutual_info_score</td>\n",
       "      <td>-0.071454</td>\n",
       "      <td>genetics</td>\n",
       "      <td>ac_ward</td>\n",
       "      <td>spcc_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>completeness_score</td>\n",
       "      <td>0.710453</td>\n",
       "      <td>media</td>\n",
       "      <td>ac_ward</td>\n",
       "      <td>spcc_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>completeness_score</td>\n",
       "      <td>0.199391</td>\n",
       "      <td>genetics</td>\n",
       "      <td>ac_ward</td>\n",
       "      <td>spcc_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>fowlkes_mallows_score</td>\n",
       "      <td>0.362421</td>\n",
       "      <td>media</td>\n",
       "      <td>ac_ward</td>\n",
       "      <td>spcc_top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>fowlkes_mallows_score</td>\n",
       "      <td>0.125311</td>\n",
       "      <td>genetics</td>\n",
       "      <td>ac_ward</td>\n",
       "      <td>spcc_top1k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>136 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        metric     value     check clustering_method  \\\n",
       "0          adjusted_rand_score  0.538265     media            kmeans   \n",
       "1          adjusted_rand_score -0.045111  genetics            kmeans   \n",
       "2   adjusted_mutual_info_score  0.716702     media            kmeans   \n",
       "3   adjusted_mutual_info_score -0.141768  genetics            kmeans   \n",
       "4           completeness_score  0.799636     media            kmeans   \n",
       "..                         ...       ...       ...               ...   \n",
       "19  adjusted_mutual_info_score -0.071454  genetics           ac_ward   \n",
       "20          completeness_score  0.710453     media           ac_ward   \n",
       "21          completeness_score  0.199391  genetics           ac_ward   \n",
       "22       fowlkes_mallows_score  0.362421     media           ac_ward   \n",
       "23       fowlkes_mallows_score  0.125311  genetics           ac_ward   \n",
       "\n",
       "          method  \n",
       "0   bonobo_top1k  \n",
       "1   bonobo_top1k  \n",
       "2   bonobo_top1k  \n",
       "3   bonobo_top1k  \n",
       "4   bonobo_top1k  \n",
       "..           ...  \n",
       "19    spcc_top1k  \n",
       "20    spcc_top1k  \n",
       "21    spcc_top1k  \n",
       "22    spcc_top1k  \n",
       "23    spcc_top1k  \n",
       "\n",
       "[136 rows x 5 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the correlation performance data\n",
    "fn_correlation = 'clustering_performance.csv'\n",
    "perf_clustering = pd.DataFrame()\n",
    "for i,val in dictionary_comparison.items():\n",
    "    with open(val+fn_correlation, 'r') as f:\n",
    "        temp = pd.read_csv(f)\n",
    "        temp['method'] = i\n",
    "    perf_clustering = pd.concat([perf_clustering, temp], axis=0)\n",
    "perf_clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split column\n",
    "perf_clustering['method_choice'] = perf_clustering.method.str.split('_').str[0]\n",
    "perf_clustering['gene_choice'] = perf_clustering.method.str.split('_').str[1]\n",
    "perf_clustering.head()\n",
    "perf_clustering.to_csv(results_tables+'clustering_performance_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "media_color = '#d6604d'\n",
    "genetics_color = '#4393c3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1895.87x1200 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('paper')\n",
    "g1 = sns.catplot(data = perf_clustering[perf_clustering['gene_choice']=='top1k'],\n",
    "            row = 'metric', \n",
    "            y = 'value', \n",
    "            hue = 'check',\n",
    "            x = 'clustering_method', \n",
    "            kind = 'bar', \n",
    "            col = 'method_choice',\n",
    "            #col_wrap = 2,\n",
    "            aspect = 1.5,\n",
    "            height=3, \n",
    "            sharey = True, \n",
    "            hue_order = ['media', 'genetics'],\n",
    "            palette = [media_color, genetics_color], )\n",
    "\n",
    "plt.tight_layout()\n",
    "g1.savefig(results_figures + 'clustering_performance_top1k.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 995.875x1200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('paper')\n",
    "g1 = sns.catplot(data = perf_clustering[perf_clustering['gene_choice']=='sparse'],\n",
    "            row = 'metric', \n",
    "            y = 'value', \n",
    "            hue = 'check',\n",
    "            x = 'clustering_method', \n",
    "            kind = 'bar', \n",
    "            col = 'method_choice',\n",
    "            #col_wrap = 2,\n",
    "            aspect = 1.5,\n",
    "            height=3, \n",
    "            sharey = True, \n",
    "            hue_order = ['media', 'genetics'],\n",
    "            palette = [media_color, genetics_color], )\n",
    "\n",
    "plt.tight_layout()\n",
    "g1.savefig(results_figures + 'clustering_performance_sparse.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "#perf_clustering[perf_clustering['gene_choice']=='random']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.set_context('paper')\n",
    "# g1 = sns.catplot(data = perf_clustering[perf_clustering['gene_choice']=='random'],\n",
    "#             row = 'metric', \n",
    "#             y = 'value', \n",
    "#             hue = 'check',\n",
    "#             x = 'clustering_method', \n",
    "#             kind = 'bar', \n",
    "#             col = 'method_choice',\n",
    "#             #col_wrap = 2,\n",
    "#             aspect = 1.5,\n",
    "#             height=3, \n",
    "#             sharey = True, \n",
    "#             hue_order = ['media', 'genetics'],\n",
    "#             palette = [media_color, genetics_color], )\n",
    "\n",
    "# plt.tight_layout()\n",
    "# g1.savefig(results_figures + 'clustering_performance_random.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "metric_dict = {'adjusted_rand_score':'ARI', 'adjusted_mutual_info_score':'AMI','completeness_score':'Completeness', 'fowlkes_mallows_score':'Fowlkes-\\nMallows', }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "perf_clustering['metric_id'] = perf_clustering['metric']\n",
    "perf_clustering['metric'] = perf_clustering['metric_id'].map(metric_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1488.72x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('notebook')\n",
    "g1 = sns.catplot(data = perf_clustering[perf_clustering['gene_choice']=='sparse'],\n",
    "            x = 'metric', \n",
    "            y = 'value', \n",
    "            row = 'check',\n",
    "            col = 'clustering_method', \n",
    "            kind = 'bar', \n",
    "            hue = 'method_choice',\n",
    "            #col_wrap = 2,\n",
    "            aspect = 1.5,\n",
    "            height=3, \n",
    "            sharey = True, \n",
    "            hue_order = ['bonobo', 'sweet'],\n",
    "            palette = [bonobo_sparse_color, sweet_sparse_color], \n",
    "            \n",
    "            )\n",
    "\n",
    "#g1.axes.flat[0].set_xticklabels(['\\n'.join(i.split('_')) for i in g1.axes.flat[0].get_xticklabels()])\n",
    "\n",
    "# format the labels with f-strings\n",
    "#for ax in g1.axes.flat:\n",
    "#    ax.set_xticklabels([metric_dict[i.get_text()] for i in ax.get_xticklabels()])\n",
    "\n",
    "sns.move_legend(g1, \"upper left\", bbox_to_anchor=(.90, .50), title='Network', )\n",
    "\n",
    "plt.tight_layout()\n",
    "g1.savefig(results_figures + 'clustering_performance_sparse_huemethod.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1488.72x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('notebook')\n",
    "g1 = sns.catplot(data = perf_clustering[perf_clustering['gene_choice']=='top1k'],\n",
    "            x = 'metric', \n",
    "            y = 'value', \n",
    "            row = 'check',\n",
    "            col = 'clustering_method', \n",
    "            kind = 'bar', \n",
    "            hue = 'method_choice',\n",
    "            #col_wrap = 2,\n",
    "            aspect = 1.5,\n",
    "            height=3, \n",
    "            sharey = True, \n",
    "            hue_order = ['bonobo', 'sweet','lioness', 'spcc'],\n",
    "            palette = [bonobo_color, sweet_color, lioness_color, spcc_color], \n",
    "            )\n",
    "\n",
    "\n",
    "# format the labels with f-strings\n",
    "#for ax in g1.axes.flat:\n",
    "#    ax.set_xticklabels([metric_dict[i.get_text()] for i in ax.get_xticklabels()])\n",
    "\n",
    "sns.move_legend(g1, \"upper left\", bbox_to_anchor=(.90, .50), title='Network', )\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "g1.savefig(results_figures + 'clustering_performance_top1k_huemethod.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sns.set_context('notebook')\n",
    "# g1 = sns.catplot(data = perf_clustering[perf_clustering['gene_choice']=='random'],\n",
    "#             x = 'metric', \n",
    "#             y = 'value', \n",
    "#             row = 'check',\n",
    "#             col = 'clustering_method', \n",
    "#             kind = 'bar', \n",
    "#             hue = 'method_choice',\n",
    "#             #col_wrap = 2,\n",
    "#             aspect = 1.5,\n",
    "#             height=3, \n",
    "#             sharey = True, \n",
    "#             hue_order = ['bonobo', 'sweet','lioness', 'spcc'],\n",
    "#             palette = [bonobo_sparse_color, sweet_sparse_color, lioness_sparse_color, spcc_sparse_color], \n",
    "#             )\n",
    "\n",
    "\n",
    "# # format the labels with f-strings\n",
    "# #for ax in g1.axes.flat:\n",
    "# #    ax.set_xticklabels([metric_dict[i.get_text()] for i in ax.get_xticklabels()])\n",
    "\n",
    "# sns.move_legend(g1, \"upper left\", bbox_to_anchor=(.92, .50), title='Network', )\n",
    "\n",
    "\n",
    "# plt.tight_layout()\n",
    "# g1.savefig(results_figures + 'clustering_performance_random_huemethod.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distnace measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>test</th>\n",
       "      <th>check</th>\n",
       "      <th>stat</th>\n",
       "      <th>pval</th>\n",
       "      <th>group</th>\n",
       "      <th>method</th>\n",
       "      <th>network</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dal82_MinimalGlucose</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>645.0</td>\n",
       "      <td>6.372237e-01</td>\n",
       "      <td>dal82</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>euclidean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dal82_MinimalGlucose</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>212.0</td>\n",
       "      <td>1.021154e-04</td>\n",
       "      <td>MinimalGlucose</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>euclidean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>721.0</td>\n",
       "      <td>8.437053e-01</td>\n",
       "      <td>dal81</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>euclidean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.211450e-08</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>euclidean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gln3_YPD</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>818.0</td>\n",
       "      <td>9.678853e-01</td>\n",
       "      <td>gln3</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>euclidean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>gzf3_YPDDiauxic</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.211450e-08</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>cosine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>gzf3_YPDRapa</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>657.0</td>\n",
       "      <td>6.754676e-01</td>\n",
       "      <td>gzf3</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>cosine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>gzf3_YPDRapa</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.211450e-08</td>\n",
       "      <td>YPDRapa</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>cosine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>gzf3_YPEtOH</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>652.0</td>\n",
       "      <td>6.597286e-01</td>\n",
       "      <td>gzf3</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>cosine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>gzf3_YPEtOH</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>69.0</td>\n",
       "      <td>4.780696e-07</td>\n",
       "      <td>YPEtOH</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>cosine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3168 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sample test     check   stat          pval           group  \\\n",
       "0    dal82_MinimalGlucose   mw  genetics  645.0  6.372237e-01           dal82   \n",
       "1    dal82_MinimalGlucose   mw     media  212.0  1.021154e-04  MinimalGlucose   \n",
       "2        dal81_YPDDiauxic   mw  genetics  721.0  8.437053e-01           dal81   \n",
       "3        dal81_YPDDiauxic   mw     media    0.0  2.211450e-08      YPDDiauxic   \n",
       "4                gln3_YPD   mw  genetics  818.0  9.678853e-01            gln3   \n",
       "..                    ...  ...       ...    ...           ...             ...   \n",
       "523       gzf3_YPDDiauxic   mw     media    0.0  2.211450e-08      YPDDiauxic   \n",
       "524          gzf3_YPDRapa   mw  genetics  657.0  6.754676e-01            gzf3   \n",
       "525          gzf3_YPDRapa   mw     media    0.0  2.211450e-08         YPDRapa   \n",
       "526           gzf3_YPEtOH   mw  genetics  652.0  6.597286e-01            gzf3   \n",
       "527           gzf3_YPEtOH   mw     media   69.0  4.780696e-07          YPEtOH   \n",
       "\n",
       "           method       network   distance  \n",
       "0    bonobo_top1k  bonobo_top1k  euclidean  \n",
       "1    bonobo_top1k  bonobo_top1k  euclidean  \n",
       "2    bonobo_top1k  bonobo_top1k  euclidean  \n",
       "3    bonobo_top1k  bonobo_top1k  euclidean  \n",
       "4    bonobo_top1k  bonobo_top1k  euclidean  \n",
       "..            ...           ...        ...  \n",
       "523    spcc_top1k    spcc_top1k     cosine  \n",
       "524    spcc_top1k    spcc_top1k     cosine  \n",
       "525    spcc_top1k    spcc_top1k     cosine  \n",
       "526    spcc_top1k    spcc_top1k     cosine  \n",
       "527    spcc_top1k    spcc_top1k     cosine  \n",
       "\n",
       "[3168 rows x 9 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the correlation performance data\n",
    "\n",
    "fn_correlation = 'network_distance_performance.csv'\n",
    "perf_clustering = pd.DataFrame()\n",
    "for i,val in dictionary_comparison.items():\n",
    "    with open(val+fn_correlation, 'r') as f:\n",
    "        temp = pd.read_csv(f)\n",
    "        temp['distance'] = temp['method'].copy()\n",
    "        temp['method'] = i\n",
    "    perf_clustering = pd.concat([perf_clustering, temp], axis=0)\n",
    "perf_clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split column\n",
    "perf_clustering['method_choice'] = perf_clustering.method.str.split('_').str[0]\n",
    "perf_clustering['gene_choice'] = perf_clustering.method.str.split('_').str[1]\n",
    "perf_clustering.head()\n",
    "perf_clustering.to_csv(results_tables+'network_distance_performance_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>test</th>\n",
       "      <th>check</th>\n",
       "      <th>stat</th>\n",
       "      <th>pval</th>\n",
       "      <th>group</th>\n",
       "      <th>method</th>\n",
       "      <th>network</th>\n",
       "      <th>distance</th>\n",
       "      <th>method_choice</th>\n",
       "      <th>gene_choice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dal82_MinimalGlucose</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>645.0</td>\n",
       "      <td>6.372237e-01</td>\n",
       "      <td>dal82</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>bonobo</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dal82_MinimalGlucose</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>212.0</td>\n",
       "      <td>1.021154e-04</td>\n",
       "      <td>MinimalGlucose</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>bonobo</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>721.0</td>\n",
       "      <td>8.437053e-01</td>\n",
       "      <td>dal81</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>bonobo</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.211450e-08</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>bonobo</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gln3_YPD</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>818.0</td>\n",
       "      <td>9.678853e-01</td>\n",
       "      <td>gln3</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>bonobo</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>gzf3_YPDDiauxic</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.211450e-08</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>cosine</td>\n",
       "      <td>spcc</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>gzf3_YPDRapa</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>657.0</td>\n",
       "      <td>6.754676e-01</td>\n",
       "      <td>gzf3</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>cosine</td>\n",
       "      <td>spcc</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>gzf3_YPDRapa</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.211450e-08</td>\n",
       "      <td>YPDRapa</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>cosine</td>\n",
       "      <td>spcc</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>gzf3_YPEtOH</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>652.0</td>\n",
       "      <td>6.597286e-01</td>\n",
       "      <td>gzf3</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>cosine</td>\n",
       "      <td>spcc</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>gzf3_YPEtOH</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>69.0</td>\n",
       "      <td>4.780696e-07</td>\n",
       "      <td>YPEtOH</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>cosine</td>\n",
       "      <td>spcc</td>\n",
       "      <td>top1k</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3168 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sample test     check   stat          pval           group  \\\n",
       "0    dal82_MinimalGlucose   mw  genetics  645.0  6.372237e-01           dal82   \n",
       "1    dal82_MinimalGlucose   mw     media  212.0  1.021154e-04  MinimalGlucose   \n",
       "2        dal81_YPDDiauxic   mw  genetics  721.0  8.437053e-01           dal81   \n",
       "3        dal81_YPDDiauxic   mw     media    0.0  2.211450e-08      YPDDiauxic   \n",
       "4                gln3_YPD   mw  genetics  818.0  9.678853e-01            gln3   \n",
       "..                    ...  ...       ...    ...           ...             ...   \n",
       "523       gzf3_YPDDiauxic   mw     media    0.0  2.211450e-08      YPDDiauxic   \n",
       "524          gzf3_YPDRapa   mw  genetics  657.0  6.754676e-01            gzf3   \n",
       "525          gzf3_YPDRapa   mw     media    0.0  2.211450e-08         YPDRapa   \n",
       "526           gzf3_YPEtOH   mw  genetics  652.0  6.597286e-01            gzf3   \n",
       "527           gzf3_YPEtOH   mw     media   69.0  4.780696e-07          YPEtOH   \n",
       "\n",
       "           method       network   distance method_choice gene_choice  \n",
       "0    bonobo_top1k  bonobo_top1k  euclidean        bonobo       top1k  \n",
       "1    bonobo_top1k  bonobo_top1k  euclidean        bonobo       top1k  \n",
       "2    bonobo_top1k  bonobo_top1k  euclidean        bonobo       top1k  \n",
       "3    bonobo_top1k  bonobo_top1k  euclidean        bonobo       top1k  \n",
       "4    bonobo_top1k  bonobo_top1k  euclidean        bonobo       top1k  \n",
       "..            ...           ...        ...           ...         ...  \n",
       "523    spcc_top1k    spcc_top1k     cosine          spcc       top1k  \n",
       "524    spcc_top1k    spcc_top1k     cosine          spcc       top1k  \n",
       "525    spcc_top1k    spcc_top1k     cosine          spcc       top1k  \n",
       "526    spcc_top1k    spcc_top1k     cosine          spcc       top1k  \n",
       "527    spcc_top1k    spcc_top1k     cosine          spcc       top1k  \n",
       "\n",
       "[3168 rows x 11 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sample</th>\n",
       "      <th>test</th>\n",
       "      <th>check</th>\n",
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       "      <th>0</th>\n",
       "      <td>dal82_MinimalGlucose</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>645.0</td>\n",
       "      <td>6.372237e-01</td>\n",
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       "      <td>MinimalGlucose</td>\n",
       "      <td>bonobo_top1k</td>\n",
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       "      <td>bonobo</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>721.0</td>\n",
       "      <td>8.437053e-01</td>\n",
       "      <td>dal81</td>\n",
       "      <td>bonobo_top1k</td>\n",
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       "      <td>bonobo</td>\n",
       "      <td>top1k</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>gzf3_YPDDiauxic</td>\n",
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       "      <td>2.211450e-08</td>\n",
       "      <td>YPDDiauxic</td>\n",
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       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>gzf3_YPDRapa</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>657.0</td>\n",
       "      <td>6.754676e-01</td>\n",
       "      <td>gzf3</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>cosine</td>\n",
       "      <td>spcc</td>\n",
       "      <td>top1k</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>gzf3_YPDRapa</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.211450e-08</td>\n",
       "      <td>YPDRapa</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>cosine</td>\n",
       "      <td>spcc</td>\n",
       "      <td>top1k</td>\n",
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       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>gzf3_YPEtOH</td>\n",
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       "      <td>genetics</td>\n",
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       "      <td>gzf3</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>cosine</td>\n",
       "      <td>spcc</td>\n",
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       "      <td>gzf3_YPEtOH</td>\n",
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       "      <td>69.0</td>\n",
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       "      <td>top1k</td>\n",
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       "<p>3168 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sample test     check   stat          pval           group  \\\n",
       "0    dal82_MinimalGlucose   mw  genetics  645.0  6.372237e-01           dal82   \n",
       "1    dal82_MinimalGlucose   mw     media  212.0  1.021154e-04  MinimalGlucose   \n",
       "2        dal81_YPDDiauxic   mw  genetics  721.0  8.437053e-01           dal81   \n",
       "3        dal81_YPDDiauxic   mw     media    0.0  2.211450e-08      YPDDiauxic   \n",
       "4                gln3_YPD   mw  genetics  818.0  9.678853e-01            gln3   \n",
       "..                    ...  ...       ...    ...           ...             ...   \n",
       "523       gzf3_YPDDiauxic   mw     media    0.0  2.211450e-08      YPDDiauxic   \n",
       "524          gzf3_YPDRapa   mw  genetics  657.0  6.754676e-01            gzf3   \n",
       "525          gzf3_YPDRapa   mw     media    0.0  2.211450e-08         YPDRapa   \n",
       "526           gzf3_YPEtOH   mw  genetics  652.0  6.597286e-01            gzf3   \n",
       "527           gzf3_YPEtOH   mw     media   69.0  4.780696e-07          YPEtOH   \n",
       "\n",
       "           method       network   distance method_choice gene_choice  \\\n",
       "0    bonobo_top1k  bonobo_top1k  euclidean        bonobo       top1k   \n",
       "1    bonobo_top1k  bonobo_top1k  euclidean        bonobo       top1k   \n",
       "2    bonobo_top1k  bonobo_top1k  euclidean        bonobo       top1k   \n",
       "3    bonobo_top1k  bonobo_top1k  euclidean        bonobo       top1k   \n",
       "4    bonobo_top1k  bonobo_top1k  euclidean        bonobo       top1k   \n",
       "..            ...           ...        ...           ...         ...   \n",
       "523    spcc_top1k    spcc_top1k     cosine          spcc       top1k   \n",
       "524    spcc_top1k    spcc_top1k     cosine          spcc       top1k   \n",
       "525    spcc_top1k    spcc_top1k     cosine          spcc       top1k   \n",
       "526    spcc_top1k    spcc_top1k     cosine          spcc       top1k   \n",
       "527    spcc_top1k    spcc_top1k     cosine          spcc       top1k   \n",
       "\n",
       "     significant  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              1  \n",
       "4              0  \n",
       "..           ...  \n",
       "523            1  \n",
       "524            0  \n",
       "525            1  \n",
       "526            0  \n",
       "527            1  \n",
       "\n",
       "[3168 rows x 12 columns]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_clustering = perf_clustering[perf_clustering['gene_choice']!='random']\n",
    "perf_clustering['significant'] = 1*(perf_clustering['pval'] < (0.05/len(perf_clustering)))\n",
    "perf_clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
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       "      <th>38</th>\n",
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       "      <td>WT(ho)_AmmoniumSulfate</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>WT(ho)_AmmoniumSulfate</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>580.0</td>\n",
       "      <td>4.159123e-01</td>\n",
       "      <td>WT(ho)</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>bonobo_top1k</td>\n",
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       "      <td>bonobo</td>\n",
       "      <td>top1k</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>WT(ho)_AmmoniumSulfate</td>\n",
       "      <td>mw</td>\n",
       "      <td>genetics</td>\n",
       "      <td>555.0</td>\n",
       "      <td>3.339404e-01</td>\n",
       "      <td>WT(ho)</td>\n",
       "      <td>lioness_top1k</td>\n",
       "      <td>lioness_top1k</td>\n",
       "      <td>cosine</td>\n",
       "      <td>lioness</td>\n",
       "      <td>top1k</td>\n",
       "      <td>0</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>stp2_YPEtOH</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.665811e-08</td>\n",
       "      <td>YPEtOH</td>\n",
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       "      <td>euclidean</td>\n",
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       "      <td>top1k</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>stp2_YPEtOH</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>146.0</td>\n",
       "      <td>1.015535e-05</td>\n",
       "      <td>YPEtOH</td>\n",
       "      <td>sweet_sparse</td>\n",
       "      <td>sweet_sparse</td>\n",
       "      <td>cosine</td>\n",
       "      <td>sweet</td>\n",
       "      <td>sparse</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>stp2_YPEtOH</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>144.0</td>\n",
       "      <td>9.427090e-06</td>\n",
       "      <td>YPEtOH</td>\n",
       "      <td>sweet_sparse</td>\n",
       "      <td>sweet_sparse</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>sweet</td>\n",
       "      <td>sparse</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>stp2_YPEtOH</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>265.0</td>\n",
       "      <td>5.303949e-04</td>\n",
       "      <td>YPEtOH</td>\n",
       "      <td>sweet_top1k</td>\n",
       "      <td>sweet_top1k</td>\n",
       "      <td>cosine</td>\n",
       "      <td>sweet</td>\n",
       "      <td>top1k</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>stp2_YPEtOH</td>\n",
       "      <td>mw</td>\n",
       "      <td>media</td>\n",
       "      <td>243.0</td>\n",
       "      <td>2.736589e-04</td>\n",
       "      <td>YPEtOH</td>\n",
       "      <td>sweet_top1k</td>\n",
       "      <td>sweet_top1k</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>sweet</td>\n",
       "      <td>top1k</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3168 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     sample test     check   stat          pval   group  \\\n",
       "302  WT(ho)_AmmoniumSulfate   mw  genetics  519.0  2.293163e-01  WT(ho)   \n",
       "38   WT(ho)_AmmoniumSulfate   mw  genetics  555.0  3.339404e-01  WT(ho)   \n",
       "302  WT(ho)_AmmoniumSulfate   mw  genetics  574.0  3.957477e-01  WT(ho)   \n",
       "38   WT(ho)_AmmoniumSulfate   mw  genetics  580.0  4.159123e-01  WT(ho)   \n",
       "314  WT(ho)_AmmoniumSulfate   mw  genetics  555.0  3.339404e-01  WT(ho)   \n",
       "..                      ...  ...       ...    ...           ...     ...   \n",
       "131             stp2_YPEtOH   mw     media    4.0  2.665811e-08  YPEtOH   \n",
       "315             stp2_YPEtOH   mw     media  146.0  1.015535e-05  YPEtOH   \n",
       "51              stp2_YPEtOH   mw     media  144.0  9.427090e-06  YPEtOH   \n",
       "315             stp2_YPEtOH   mw     media  265.0  5.303949e-04  YPEtOH   \n",
       "51              stp2_YPEtOH   mw     media  243.0  2.736589e-04  YPEtOH   \n",
       "\n",
       "            method        network   distance method_choice gene_choice  \\\n",
       "302  bonobo_sparse  bonobo_sparse     cosine        bonobo      sparse   \n",
       "38   bonobo_sparse  bonobo_sparse  euclidean        bonobo      sparse   \n",
       "302   bonobo_top1k   bonobo_top1k     cosine        bonobo       top1k   \n",
       "38    bonobo_top1k   bonobo_top1k  euclidean        bonobo       top1k   \n",
       "314  lioness_top1k  lioness_top1k     cosine       lioness       top1k   \n",
       "..             ...            ...        ...           ...         ...   \n",
       "131     spcc_top1k     spcc_top1k  euclidean          spcc       top1k   \n",
       "315   sweet_sparse   sweet_sparse     cosine         sweet      sparse   \n",
       "51    sweet_sparse   sweet_sparse  euclidean         sweet      sparse   \n",
       "315    sweet_top1k    sweet_top1k     cosine         sweet       top1k   \n",
       "51     sweet_top1k    sweet_top1k  euclidean         sweet       top1k   \n",
       "\n",
       "     significant  \n",
       "302            0  \n",
       "38             0  \n",
       "302            0  \n",
       "38             0  \n",
       "314            0  \n",
       "..           ...  \n",
       "131            1  \n",
       "315            1  \n",
       "51             1  \n",
       "315            0  \n",
       "51             0  \n",
       "\n",
       "[3168 rows x 12 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perf_clustering.sort_values(by = ['sample','test', 'check', 'method','distance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "method_order = ['bonobo_top1k', 'bonobo_sparse', 'sweet_top1k', 'sweet_sparse', 'lioness_top1k', 'spcc_top1k']\n",
    "method_palette = [bonobo_color, bonobo_sparse_color, sweet_color, sweet_sparse_color, lioness_color, spcc_color]\n",
    "#method_palette = ['#fb9a99', '#e31a1c', '#a6cee3', '#1f78b4', '#b2df8a', '#6a3d9a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 970.875x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g1 = sns.catplot(data = perf_clustering[perf_clustering['significant']==1], y = 'group', hue = 'method', kind = 'count', aspect = .8,\n",
    "            palette = method_palette, hue_order=method_order, col = 'distance')\n",
    "g1.set_titles('Distance: {col_name}')\n",
    "g1.set_xlabels('Number of significant samples')\n",
    "g1.fig.savefig(results_figures + 'network_distance_performance_significant_samples.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 970.875x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perf_clustering['significant'] = 1*(perf_clustering['pval'] < (0.05))\n",
    "perf_clustering\n",
    "\n",
    "g1 = sns.catplot(data = perf_clustering[perf_clustering['significant']==1], y = 'group', hue = 'method', kind = 'count', aspect = .8,\n",
    "            palette = method_palette, hue_order=method_order, col = 'distance')\n",
    "g1.set_titles('Distance: {col_name}')\n",
    "g1.set_xlabels('Number of significant samples')\n",
    "g1.fig.savefig(results_figures + 'network_distance_performance_significant_samples_nobonferroni.pdf', bbox_inches = 'tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>method</th>\n",
       "      <th>check</th>\n",
       "      <th>distance</th>\n",
       "      <th>sample</th>\n",
       "      <th>test</th>\n",
       "      <th>stat</th>\n",
       "      <th>pval</th>\n",
       "      <th>network</th>\n",
       "      <th>method_choice</th>\n",
       "      <th>gene_choice</th>\n",
       "      <th>significant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AmmoniumSulfate</td>\n",
       "      <td>bonobo_sparse</td>\n",
       "      <td>media</td>\n",
       "      <td>cosine</td>\n",
       "      <td>stp1_AmmoniumSulfateWT(ho)_AmmoniumSulfatedal8...</td>\n",
       "      <td>mwmwmwmwmwmwmwmwmwmwmwmw</td>\n",
       "      <td>1722.0</td>\n",
       "      <td>0.000142</td>\n",
       "      <td>bonobo_sparsebonobo_sparsebonobo_sparsebonobo_...</td>\n",
       "      <td>bonobobonobobonobobonobobonobobonobobonobobono...</td>\n",
       "      <td>sparsesparsesparsesparsesparsesparsesparsespar...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AmmoniumSulfate</td>\n",
       "      <td>bonobo_sparse</td>\n",
       "      <td>media</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>stp1_AmmoniumSulfateWT(ho)_AmmoniumSulfatedal8...</td>\n",
       "      <td>mwmwmwmwmwmwmwmwmwmwmwmw</td>\n",
       "      <td>1665.0</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>bonobo_sparsebonobo_sparsebonobo_sparsebonobo_...</td>\n",
       "      <td>bonobobonobobonobobonobobonobobonobobonobobono...</td>\n",
       "      <td>sparsesparsesparsesparsesparsesparsesparsespar...</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AmmoniumSulfate</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>media</td>\n",
       "      <td>cosine</td>\n",
       "      <td>stp1_AmmoniumSulfateWT(ho)_AmmoniumSulfatedal8...</td>\n",
       "      <td>mwmwmwmwmwmwmwmwmwmwmwmw</td>\n",
       "      <td>4395.0</td>\n",
       "      <td>0.186978</td>\n",
       "      <td>bonobo_top1kbonobo_top1kbonobo_top1kbonobo_top...</td>\n",
       "      <td>bonobobonobobonobobonobobonobobonobobonobobono...</td>\n",
       "      <td>top1ktop1ktop1ktop1ktop1ktop1ktop1ktop1ktop1kt...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AmmoniumSulfate</td>\n",
       "      <td>bonobo_top1k</td>\n",
       "      <td>media</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>stp1_AmmoniumSulfateWT(ho)_AmmoniumSulfatedal8...</td>\n",
       "      <td>mwmwmwmwmwmwmwmwmwmwmwmw</td>\n",
       "      <td>4893.0</td>\n",
       "      <td>0.581391</td>\n",
       "      <td>bonobo_top1kbonobo_top1kbonobo_top1kbonobo_top...</td>\n",
       "      <td>bonobobonobobonobobonobobonobobonobobonobobono...</td>\n",
       "      <td>top1ktop1ktop1ktop1ktop1ktop1ktop1ktop1ktop1kt...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AmmoniumSulfate</td>\n",
       "      <td>lioness_top1k</td>\n",
       "      <td>media</td>\n",
       "      <td>cosine</td>\n",
       "      <td>gcn4_AmmoniumSulfateWT(ho)_AmmoniumSulfategzf3...</td>\n",
       "      <td>mwmwmwmwmwmwmwmwmwmwmwmw</td>\n",
       "      <td>5248.0</td>\n",
       "      <td>0.474056</td>\n",
       "      <td>lioness_top1klioness_top1klioness_top1klioness...</td>\n",
       "      <td>lionesslionesslionesslionesslionesslionesslion...</td>\n",
       "      <td>top1ktop1ktop1ktop1ktop1ktop1ktop1ktop1ktop1kt...</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>stp2</td>\n",
       "      <td>spcc_top1k</td>\n",
       "      <td>genetics</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>stp2_AmmoniumSulfatestp2_CStarvestp2_Glutamine...</td>\n",
       "      <td>mwmwmwmwmwmwmwmwmwmwmw</td>\n",
       "      <td>7512.0</td>\n",
       "      <td>7.818216</td>\n",
       "      <td>spcc_top1kspcc_top1kspcc_top1kspcc_top1kspcc_t...</td>\n",
       "      <td>spccspccspccspccspccspccspccspccspccspccspcc</td>\n",
       "      <td>top1ktop1ktop1ktop1ktop1ktop1ktop1ktop1ktop1kt...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>stp2</td>\n",
       "      <td>sweet_sparse</td>\n",
       "      <td>genetics</td>\n",
       "      <td>cosine</td>\n",
       "      <td>stp2_MinimalGlucosestp2_Prolinestp2_YPEtOHstp2...</td>\n",
       "      <td>mwmwmwmwmwmwmwmwmwmwmw</td>\n",
       "      <td>6134.0</td>\n",
       "      <td>3.777304</td>\n",
       "      <td>sweet_sparsesweet_sparsesweet_sparsesweet_spar...</td>\n",
       "      <td>sweetsweetsweetsweetsweetsweetsweetsweetsweets...</td>\n",
       "      <td>sparsesparsesparsesparsesparsesparsesparsespar...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>stp2</td>\n",
       "      <td>sweet_sparse</td>\n",
       "      <td>genetics</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>stp2_MinimalGlucosestp2_Prolinestp2_YPEtOHstp2...</td>\n",
       "      <td>mwmwmwmwmwmwmwmwmwmwmw</td>\n",
       "      <td>6249.0</td>\n",
       "      <td>4.178608</td>\n",
       "      <td>sweet_sparsesweet_sparsesweet_sparsesweet_spar...</td>\n",
       "      <td>sweetsweetsweetsweetsweetsweetsweetsweetsweets...</td>\n",
       "      <td>sparsesparsesparsesparsesparsesparsesparsespar...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>stp2</td>\n",
       "      <td>sweet_top1k</td>\n",
       "      <td>genetics</td>\n",
       "      <td>cosine</td>\n",
       "      <td>stp2_MinimalGlucosestp2_Prolinestp2_YPEtOHstp2...</td>\n",
       "      <td>mwmwmwmwmwmwmwmwmwmwmw</td>\n",
       "      <td>6219.0</td>\n",
       "      <td>4.061115</td>\n",
       "      <td>sweet_top1ksweet_top1ksweet_top1ksweet_top1ksw...</td>\n",
       "      <td>sweetsweetsweetsweetsweetsweetsweetsweetsweets...</td>\n",
       "      <td>top1ktop1ktop1ktop1ktop1ktop1ktop1ktop1ktop1kt...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>stp2</td>\n",
       "      <td>sweet_top1k</td>\n",
       "      <td>genetics</td>\n",
       "      <td>euclidean</td>\n",
       "      <td>stp2_MinimalGlucosestp2_Prolinestp2_YPEtOHstp2...</td>\n",
       "      <td>mwmwmwmwmwmwmwmwmwmwmw</td>\n",
       "      <td>6275.0</td>\n",
       "      <td>4.253100</td>\n",
       "      <td>sweet_top1ksweet_top1ksweet_top1ksweet_top1ksw...</td>\n",
       "      <td>sweetsweetsweetsweetsweetsweetsweetsweetsweets...</td>\n",
       "      <td>top1ktop1ktop1ktop1ktop1ktop1ktop1ktop1ktop1kt...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>276 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               group         method     check   distance  \\\n",
       "0    AmmoniumSulfate  bonobo_sparse     media     cosine   \n",
       "1    AmmoniumSulfate  bonobo_sparse     media  euclidean   \n",
       "2    AmmoniumSulfate   bonobo_top1k     media     cosine   \n",
       "3    AmmoniumSulfate   bonobo_top1k     media  euclidean   \n",
       "4    AmmoniumSulfate  lioness_top1k     media     cosine   \n",
       "..               ...            ...       ...        ...   \n",
       "271             stp2     spcc_top1k  genetics  euclidean   \n",
       "272             stp2   sweet_sparse  genetics     cosine   \n",
       "273             stp2   sweet_sparse  genetics  euclidean   \n",
       "274             stp2    sweet_top1k  genetics     cosine   \n",
       "275             stp2    sweet_top1k  genetics  euclidean   \n",
       "\n",
       "                                                sample  \\\n",
       "0    stp1_AmmoniumSulfateWT(ho)_AmmoniumSulfatedal8...   \n",
       "1    stp1_AmmoniumSulfateWT(ho)_AmmoniumSulfatedal8...   \n",
       "2    stp1_AmmoniumSulfateWT(ho)_AmmoniumSulfatedal8...   \n",
       "3    stp1_AmmoniumSulfateWT(ho)_AmmoniumSulfatedal8...   \n",
       "4    gcn4_AmmoniumSulfateWT(ho)_AmmoniumSulfategzf3...   \n",
       "..                                                 ...   \n",
       "271  stp2_AmmoniumSulfatestp2_CStarvestp2_Glutamine...   \n",
       "272  stp2_MinimalGlucosestp2_Prolinestp2_YPEtOHstp2...   \n",
       "273  stp2_MinimalGlucosestp2_Prolinestp2_YPEtOHstp2...   \n",
       "274  stp2_MinimalGlucosestp2_Prolinestp2_YPEtOHstp2...   \n",
       "275  stp2_MinimalGlucosestp2_Prolinestp2_YPEtOHstp2...   \n",
       "\n",
       "                         test    stat      pval  \\\n",
       "0    mwmwmwmwmwmwmwmwmwmwmwmw  1722.0  0.000142   \n",
       "1    mwmwmwmwmwmwmwmwmwmwmwmw  1665.0  0.000122   \n",
       "2    mwmwmwmwmwmwmwmwmwmwmwmw  4395.0  0.186978   \n",
       "3    mwmwmwmwmwmwmwmwmwmwmwmw  4893.0  0.581391   \n",
       "4    mwmwmwmwmwmwmwmwmwmwmwmw  5248.0  0.474056   \n",
       "..                        ...     ...       ...   \n",
       "271    mwmwmwmwmwmwmwmwmwmwmw  7512.0  7.818216   \n",
       "272    mwmwmwmwmwmwmwmwmwmwmw  6134.0  3.777304   \n",
       "273    mwmwmwmwmwmwmwmwmwmwmw  6249.0  4.178608   \n",
       "274    mwmwmwmwmwmwmwmwmwmwmw  6219.0  4.061115   \n",
       "275    mwmwmwmwmwmwmwmwmwmwmw  6275.0  4.253100   \n",
       "\n",
       "                                               network  \\\n",
       "0    bonobo_sparsebonobo_sparsebonobo_sparsebonobo_...   \n",
       "1    bonobo_sparsebonobo_sparsebonobo_sparsebonobo_...   \n",
       "2    bonobo_top1kbonobo_top1kbonobo_top1kbonobo_top...   \n",
       "3    bonobo_top1kbonobo_top1kbonobo_top1kbonobo_top...   \n",
       "4    lioness_top1klioness_top1klioness_top1klioness...   \n",
       "..                                                 ...   \n",
       "271  spcc_top1kspcc_top1kspcc_top1kspcc_top1kspcc_t...   \n",
       "272  sweet_sparsesweet_sparsesweet_sparsesweet_spar...   \n",
       "273  sweet_sparsesweet_sparsesweet_sparsesweet_spar...   \n",
       "274  sweet_top1ksweet_top1ksweet_top1ksweet_top1ksw...   \n",
       "275  sweet_top1ksweet_top1ksweet_top1ksweet_top1ksw...   \n",
       "\n",
       "                                         method_choice  \\\n",
       "0    bonobobonobobonobobonobobonobobonobobonobobono...   \n",
       "1    bonobobonobobonobobonobobonobobonobobonobobono...   \n",
       "2    bonobobonobobonobobonobobonobobonobobonobobono...   \n",
       "3    bonobobonobobonobobonobobonobobonobobonobobono...   \n",
       "4    lionesslionesslionesslionesslionesslionesslion...   \n",
       "..                                                 ...   \n",
       "271       spccspccspccspccspccspccspccspccspccspccspcc   \n",
       "272  sweetsweetsweetsweetsweetsweetsweetsweetsweets...   \n",
       "273  sweetsweetsweetsweetsweetsweetsweetsweetsweets...   \n",
       "274  sweetsweetsweetsweetsweetsweetsweetsweetsweets...   \n",
       "275  sweetsweetsweetsweetsweetsweetsweetsweetsweets...   \n",
       "\n",
       "                                           gene_choice  significant  \n",
       "0    sparsesparsesparsesparsesparsesparsesparsespar...           12  \n",
       "1    sparsesparsesparsesparsesparsesparsesparsespar...           12  \n",
       "2    top1ktop1ktop1ktop1ktop1ktop1ktop1ktop1ktop1kt...           11  \n",
       "3    top1ktop1ktop1ktop1ktop1ktop1ktop1ktop1ktop1kt...            9  \n",
       "4    top1ktop1ktop1ktop1ktop1ktop1ktop1ktop1ktop1kt...            9  \n",
       "..                                                 ...          ...  \n",
       "271  top1ktop1ktop1ktop1ktop1ktop1ktop1ktop1ktop1kt...            0  \n",
       "272  sparsesparsesparsesparsesparsesparsesparsespar...            0  \n",
       "273  sparsesparsesparsesparsesparsesparsesparsespar...            0  \n",
       "274  top1ktop1ktop1ktop1ktop1ktop1ktop1ktop1ktop1kt...            0  \n",
       "275  top1ktop1ktop1ktop1ktop1ktop1ktop1ktop1ktop1kt...            0  \n",
       "\n",
       "[276 rows x 12 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gperf = perf_clustering.groupby(['group', 'method','check','distance']).sum().reset_index()\n",
    "gperf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other possible analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the performance of different sparsity thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '../results/new_pseudobulk_nonzero/clustering_performance/bonobo_sparse_005/network_correlation_performance.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[190], line 34\u001b[0m\n\u001b[1;32m     32\u001b[0m perf_correlation \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame()\n\u001b[1;32m     33\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i,val \u001b[38;5;129;01min\u001b[39;00m dictionary_comparison\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m---> 34\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mval\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mfn_correlation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m     35\u001b[0m         temp \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(f)\n\u001b[1;32m     36\u001b[0m         temp[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmethod\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m i\n",
      "File \u001b[0;32m/opt/conda/envs/bonobo-env/lib/python3.9/site-packages/IPython/core/interactiveshell.py:284\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m    278\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    279\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    280\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    281\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    282\u001b[0m     )\n\u001b[0;32m--> 284\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../results/new_pseudobulk_nonzero/clustering_performance/bonobo_sparse_005/network_correlation_performance.csv'"
     ]
    }
   ],
   "source": [
    "# Cleaned and pseudobulked data\n",
    "params = {\n",
    "    'nonzero' : {'yeast_expression' : '../data/processed/pseudobulk_nonzero_logcounts.tsv',\\\n",
    "               'output_folder' : '../results/new_pseudobulk_nonzero/'},\\\n",
    "    'perc02' : {'yeast_expression': '../data/processed/pseudobulk_perc02_logcounts.tsv',\\\n",
    "              'output_folder' : '../results/new_pseudobulk_perc02/'}\n",
    "}\n",
    "params\n",
    "\n",
    "choice = 'nonzero'\n",
    "\n",
    "yeast_expression = params[choice]['yeast_expression']\n",
    "output_folder = params[choice]['output_folder']+'clustering_performance/'\n",
    "\n",
    "dictionary_comparison = {\n",
    "    'bonobo_sparse_001' : output_folder+'bonobo_sparse/',\n",
    "    'bonobo_sparse_005': output_folder+'bonobo_sparse_005/',\n",
    "    'bonobo_sparse_01' : output_folder+'bonobo_sparse_01/',\n",
    "}\n",
    "\n",
    "#os.makedirs(params[choice]['output_folder']+'new_clustering_performance/')\n",
    "if os.path.exists(params[choice]['output_folder']+'clustering_performance/comparison_bonobo') == False: \n",
    "    os.mkdir(params[choice]['output_folder']+'clustering_performance/comparison_bonobo')\n",
    "    os.mkdir(params[choice]['output_folder']+'clustering_performance/comparison_bonobo/figures')\n",
    "    os.mkdir(params[choice]['output_folder']+'clustering_performance/comparison_bonobo/tables')\n",
    "results_figures = params[choice]['output_folder']+'clustering_performance/comparison_bonobo/figures/'\n",
    "results_tables = params[choice]['output_folder']+'clustering_performance/comparison_bonobo/tables/'\n",
    "\n",
    "\n",
    "# Load the correlation performance data\n",
    "fn_correlation = 'network_correlation_performance.csv'\n",
    "perf_correlation = pd.DataFrame()\n",
    "for i,val in dictionary_comparison.items():\n",
    "    with open(val+fn_correlation, 'r') as f:\n",
    "        temp = pd.read_csv(f)\n",
    "        temp['method'] = i\n",
    "    perf_correlation = pd.concat([perf_correlation, temp], axis=0)\n",
    "perf_correlation\n",
    "\n",
    "# Split column\n",
    "perf_correlation['method_choice'] = perf_correlation.method.str.split('_').str[0]\n",
    "perf_correlation['gene_choice'] = perf_correlation.method.str.split('_').str[1]\n",
    "perf_correlation.head()\n",
    "\n",
    "\n",
    "# Plot the performance\n",
    "\n",
    "method_order = ['bonobo_sparse_001', 'bonobo_sparse_005', 'bonobo_sparse_01']\n",
    "perf_correlation_original = perf_correlation[perf_correlation.method.isin(method_order)]\n",
    "\n",
    "\n",
    "bonobo001_color = \"#e41a1c\"\n",
    "bonobo005_color = '#f8776d'\n",
    "bonobo01_color = '#ffbab5'\n",
    "\n",
    "\n",
    "palette = [bonobo001_color, bonobo005_color, bonobo01_color]\n",
    "\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "sns.set_context(\"notebook\")\n",
    "sns.set(rc={'axes.facecolor':'#EBEBEB'})\n",
    "\n",
    "\n",
    "f, ax = plt.subplots(2, 1, figsize = (10, 8))\n",
    "\n",
    "sns.violinplot(data = perf_correlation_original[perf_correlation_original.check == 'genetics'].sort_values(by = 'auc', ascending = False), \n",
    "                            y = 'auc', x = 'group', hue = 'method', ax = ax[0],\n",
    "                            #xmin = 0, xmax = 1, \n",
    "                            cut = 0, linewidth = 1, scale = 'width', inner = 'quartile',\n",
    "                            width = .7,\n",
    "                            hue_order = method_order,\n",
    "                            palette = palette\n",
    "                            )\n",
    "\n",
    "sns.violinplot(data = perf_correlation_original[perf_correlation_original.check == 'media'].sort_values(by = 'auc', ascending = False), \n",
    "                            y = 'auc', x = 'group', hue = 'method', ax = ax[1],\n",
    "                            width = .7,\n",
    "                            #xmin = 0, xmax = 1, \n",
    "                            cut = 0, linewidth = 1, scale = 'width', inner = 'quartile',\n",
    "                            hue_order = method_order,\n",
    "                            palette = palette\n",
    "                            )\n",
    "\n",
    "\n",
    "# rotate xtick labels\n",
    "#g1.set_xticklabels(rotation=90)\n",
    "\n",
    "#for ax in g1.axes.flatten():\n",
    "#    for c in ax.collections:\n",
    "        #c.set_edgecolor(c.get_facecolor())\n",
    "\n",
    "xticks = g1.axes.flat[0].get_xticks()\n",
    "# add horizontal grid lines\n",
    "for axis in ax:\n",
    "    axis.set_ylim(0, 1.1)\n",
    "    axis.xaxis.grid(True)\n",
    "    axis.axhline(0.5, ls = '--',lw= 2, color = 'red', alpha = 0.5)\n",
    "    axis.set_xlabel('')\n",
    "    #axis.tick_params(labelbottom=True)\n",
    "    #axis.annotate('Genotype', xy=(4, 1), xytext=(4, .9), fontsize=14, color = '#636363')\n",
    "    #axis.annotate('Media', xy=(16, .1), xytext=(16, .1), fontsize=14, color = '#636363')\n",
    "    #axis.set_ylim(-1, 1)\n",
    "\n",
    "ax[0].set_title('Genetics')\n",
    "ax[1].set_title('Media')\n",
    "\n",
    "ax[1].set_xticklabels([media_label_dictionary[i.get_text()] for i in ax[1].get_xticklabels()])\n",
    "    \n",
    "plt.tight_layout()\n",
    "\n",
    "sns.move_legend(g1, \"upper left\", bbox_to_anchor=(1, 1), title='Network', )\n",
    "\n",
    "#g1.ax.yaxis.grid(True)\n",
    "# Add y axis line for 0.5\n",
    "#g1.ax.axhline(0.5, ls = '--',lw= 2, color = 'red', alpha = 0.5)\n",
    "f.savefig(results_figures + 'bonobo_sparse_correlation_performance_ggplotcolors.pdf', bbox_inches = 'tight')"
   ]
  }
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